Patent Application
20240257926
Digital health technologies show high potential in real-world evidence data generation. In the past decade, the number of clinical trials with digital health technologies involved showed a compound annual growth rate of 34.1%. However, to date, multiple limitations prevent the adoption of digital health technologies. Regularly named limitations include: (1) the lack of standardization, (2) concerns of how to choose the most appropriate digital measure, (3) how to collect, analyze and interpret the captured real-world evidence, (4) difficulty in maintaining integrity of solutions in light of everchanging technology, (5) how to prepare supporting materials for regulatory submission, and (6) the lack of translation from ideation to actual practice in clinical research and clinical care.
Disclosed herein are methods, systems, and non-transitory computer readable media for building, implementing, and providing standardized digital solutions, such as target solution profiles (TSPs) and digital measurement solutions (DMSs). Generally, DMSs specify components of a full solution (e.g., particular devices, algorithms, and details for a measurement solution), and TSPs represent measurement methodologies that describe how the different components interact. These TSPs and DMSs are useful for characterizing a disease for a subject and can be provided to third parties to enable such third parties to characterize diseases. Generally, TSPs and DMSs are composed of a measurement stack comprising multiple layers, also referred to herein as components. Components are connected to adjacent components in the measurement stack, and each component is useful for the approved application of digital measurement solutions. In various embodiments, particular components of TSPs and DMSs are specifically developed for or are unique to a particular disease, or (sub)groups of patients suffering from a particular disease. In various embodiments, certain components of DMSs are interchangeable and can be swapped in and out of DMSs for various diseases.
Generally, digital measurement solutions (DMSs) are profiled into generic target solution profiles (TSPs). Therefore, various DMSs can be of a common class represented by a TSP. TSPs aim to fill the earlier mentioned gaps of standardization by describing agnostic classifications (e.g., device agnostic, device-software agnostic, algorithm-agnostic). Thus, TSPs represent a standardized class of solutions with aligned definitions and validated instrumentation.
Altogether, TSPs and DMSs, as described herein, represent standardized solutions for characterizing disease. Examples of characterizing disease include, but are not limited to, determining disease severity, determining likelihood of disease progression, and measuring treatment outcomes for a disease. A first specific benefit is that TSPs allow for harmonization between multiple assets and components, thereby improving standardization within the ecosystem. Second, the development time of standardized solutions (e.g., DMS) is significantly shortened, which allows for conservation of resources and reduction of unnecessary costs. For example, available components or assets can be repurposed for similar or identical conditions with ease. Third, TSPs allow for improved life cycle management. Qualification protocols are developed for individual TSPs which encompass various DMSs. In one scenario, this ensures that DMSs in a common class represented by a TSP perform in a similar or comparable manner. In a second scenario, when upgrades occur (e.g., when new instrumentations are developed or when new software algorithms are made available), DMSs can be efficiently evaluated using qualification protocols to ensure comparable solutions of DMSs within the class of solutions. Thus, introduction of TSPs and DMSs to the ecosystem accelerates the adoption of digital measures and long-term research interoperability (e.g., interoperability across different clinical trials) first in clinical research and additionally in clinical care.
Disclosed herein is a method for characterizing a disease of a subject, the method comprising: obtaining a measurement of interest from the subject; selecting a digital measurement solution from a plurality of digital measurement solutions, wherein the plurality of digital measurement solutions are of a common class that is represented by a target solution profile; and applying the selected digital measurement solution to the obtained measurement of interest to characterize the disease for the subject, wherein the digital measurement solution comprises: a measurement definition defining one or more concepts of interest relevant to the disease; an instrumentation asset that transforms the measurement of interest captured according to the measurement definition to a dataset that is informative for characterizing the disease, wherein the instrumentation asset of the digital measurement solution is specific for a device used to capture the measurement of interest; and optionally, an evidence asset for performing one or more validations on the dataset generated by the instrumentation asset, wherein the target solution profile is unchanged over time and enables efficient life-cycle management of the plurality of digital measurement solutions. In various embodiments, the target solution profile represents a generalization of the plurality of digital measurement solutions, wherein an instrumentation asset of the target solution profile is device technology agnostic. In various embodiments, performing the one or more validations comprises performing one or more of a technical validation, an analytical validation, or a clinical validation. In various embodiments, performing the technical validation comprises comparing the dataset generated by the instrumentation asset to specifications of one or more devices used to capture the measurement of interest. In various embodiments, performing the analytical validation comprises: determining any of reliability, specificity, or sensitivity metrics for the dataset; and comparing the reliability, specificity, or sensitivity metrics to a threshold value. In various embodiments, performing the clinical validation comprises: assessing treatment effects on measurements of interest for the disease.
In various embodiments, the digital measurement solution is previously validated by implementing one or more qualification protocols used to establish comparability of solutions across the digital measurement solutions of the target solution profile. In various embodiments, a qualification protocol comprises steps of: a) recruiting a N member participant group; b) capturing measurements of interest across the N member participant group according to a specification of the digital measurement solution; c) transforming the measurements of interest into a dataset according to the specification; and d) validating the dataset to determine whether the digital measurement solution achieves comparable solutions of the target solution profile. In various embodiments, validating the dataset comprises: determining whether a characteristic of the dataset satisfies a threshold value of the target solution profile; and responsive to the determination that the characteristic of the dataset satisfies the threshold value, validating the digital measurement solution as achieving comparability of solutions. In various embodiments, validating the dataset further comprises responsive to determining that the digital measurement solution achieves comparability of solutions, storing an indication of a successful validation in metadata of the digital measurement solution. In various embodiments, the metadata of the digital measurement solution is stored in a catalog accessible for inspection by third party users. In various embodiments, the specification of the digital measurement solution represents an upgraded capability in comparison to a prior version of the digital measurement solution. In various embodiments, the specification of the digital measurement solution represents an upgraded capability included in a newly released device used to capture the measurement of interest. In various embodiments, the upgraded capability is one of an upgraded battery, upgraded data storage, upgraded acquisition frequency, or upgraded data collection algorithm. In various embodiments, the common class of the plurality of digital measurement solutions represents a common method of measuring activity from an individual. In various embodiments, the common method of measuring activity uses a class of devices comprising one or more of wearable devices, devices including accelerometers, devices including gyroscopes, ingestibles, image and voice based devices, touchless sensors, and sensor based smart devices (e.g., scales, thermometers, and respirators). In various embodiments, the instrumentation asset comprises a machine learning algorithm that transforms data captured according to the measurement definition to the dataset.
Additionally disclosed herein is a method for building a digital measurement solution for characterizing a disease, the method comprising: generating a measurement definition of a target solution profile, the measurement definition defining one or more concepts of interest relevant to the disease; generating or selecting an instrumentation asset for the target solution profile, the instrumentation asset configured to transform data captured according to the measurement definition to a dataset, the instrumentation asset being device technology agnostic and is thereby interchangeable across different target solution profiles; generating an evidence asset of the target solution profile for performing one or more validations on the dataset generated by the instrumentation asset; generating a digital measurement solution by at least specifying a device for the instrumentation asset of target solution profile, wherein the digital measurement solution is of a common class that is represented by the target solution profile, wherein the target solution profile is unchanged over time and thereby enables efficient life-cycle management of the plurality of digital measurement solutions.
In various embodiments, the one or more concepts of interest relevant to the disease comprise medical measurements of the disease or measurable experiences of individuals suffering from the disease. In various embodiments, device technology agnostic comprises one or both of being device-agnostic and being device-version agnostic. In various embodiments, methods disclosed herein further comprise implementing a qualification protocol to validate the digital measurement solution, the qualification protocol used to establish comparability of solutions across the plurality of digital measurement solutions of the target solution profile. In various embodiments, a qualification protocol comprises steps of: a) recruiting a N member participant group; b) capturing measurements of interest across the N member participant group using a specification of the digital measurement solution; c) transforming the measurements of interest into a dataset according to the specification; and d) validating the dataset to determine whether the digital measurement solution achieves comparable solutions of the target solution profile. In various embodiments, validating the dataset comprises determining whether the dataset satisfies a threshold value of the target solution profile; and responsive to the determination that the dataset satisfies the threshold value, validating the digital measurement solution as achieving comparability of solutions. In various embodiments, validating the dataset further comprises responsive to determining that the digital measurement solution achieves comparability of solutions, storing an indication of a successful validation in metadata of the digital measurement solution. In various embodiments, the metadata of the digital measurement solution is stored in a catalog accessible for inspection by third party users. In various embodiments, the specification of the digital measurement solution represents an upgraded capability of a prior version of the digital measurement solution. In various embodiments, the specification of the digital measurement solution represents an upgraded capability included in a newly released device used to capture the measurement of interest. In various embodiments, the upgraded capability is one of an upgraded battery, upgraded data storage, upgraded acquisition frequency, or upgraded data collection algorithm.
In various embodiments, the measurement definition and evidence asset are fixed for the target solution profile and specific for the disease. In various embodiments, the instrumentation asset of the target solution profile is interchangeable across different target solution profiles for characterizing a same disease or different diseases. In various embodiments, the instrumentation asset is specific for a common method of measuring activity from an individual. In various embodiments, the common method of measuring activity uses a class of devices comprising one or more of wearable devices, devices including accelerometers, devices including gyroscopes, ingestibles, image and voice based devices, touchless sensors, and sensor based smart devices (e.g., scales, thermometers, and respirators). In various embodiments, the digital measurement solution comprises providing the digital measurement solution to a third party for regulatory approval. In various embodiments, the digital measurement solution comprises providing input to the third party on one or more assets of the digital measurement solution.
In various embodiments, methods disclosed herein further comprise providing the digital measurement solution to a third party for regulatory approval. In various embodiments, methods disclosed herein further comprise providing input to the third party on one or more assets of the digital measurement solution.
In various embodiments, the digital measurement solution is one of the digital measurement solutions shown in Table 5. In various embodiments, the target solution profile is one of the target solution profiles shown in Table 4. In various embodiments, the disease is a condition shown in Table 1. In various embodiments, the one or more concepts of interest are selected from a concept of interest shown in Table 3.
Additionally disclosed herein is a method for providing one or more digital measurement solutions useful for characterizing a disease, the method comprising: providing a catalogue comprising a plurality of target solution profiles, wherein each of one or more of the target solution profiles comprises: a measurement definition of the target solution profile defining one or more concepts of interest relevant to the disease; an instrumentation asset that transforms a measurement of interest captured according to the measurement definition to a dataset that is informative for characterizing the disease, the instrumentation asset being device technology agnostic and is thereby interchangeable across different target solution profiles; and an evidence asset for performing one or more validations on the dataset generated by the instrumentation asset; receiving, from a third party, a selection of one of the target solution profiles; and providing one or more digital measurement solutions useful for characterizing the disease to the third party, wherein the one or more digital measurement solutions are of a common class represented by the selected target solution profile.
In various embodiments, methods disclosed herein further comprise: receiving, from the third party, a search query; for each of the one or more target solution profiles in the plurality of target solution profiles, evaluating the target solution profile to determine whether the target solution profile satisfies the query; and returning a list of target solution profiles that satisfy the query. In various embodiments, evaluating the target solution profile comprises: evaluating one or more components of the measurement definition for a concept of interest that satisfies the query. In various embodiments, methods disclosed herein further comprise: replacing an instrumentation asset of one of the one or more digital measurement solutions with a second instrumentation asset to generate a revised digital measurement solution; and providing the revised digital measurement solution to the third party. In various embodiments, methods disclosed herein further comprise receiving, from a third party, a suggested target solution profile not present in the provided catalog; and further generating the suggested target solution profile for inclusion in the catalog.
In various embodiments, the target solution profile comprises a measurement stack comprising a plurality of components. In various embodiments, each of the measurement definition, the instrumentation asset, and the evidence asset are represented by one or more components in the plurality of components. In various embodiments, an order of the plurality of components comprises one or more components of the measurement definition, followed by one or more components of the instrumentation asset, and further followed by one or more components of the evidence asset. In various embodiments, a component of the measurement definition interfaces with a component of the instrumentation asset, and a component of the instrumentation asset interfaces with the evidence asset. In various embodiments, the one or more components of the measurement definition comprise one or more of a hypothesis of the disease and a measurable concept of interest. In various embodiments, the one or more components of the instrumentation asset comprise one or more of a measurement method, raw data, and a machine learning algorithm. In various embodiments, the one or more components of the evidence asset comprise one or more of a technical validation, an analytical validation, and a clinical validation. In various embodiments, the one or more components of the measurement definition comprise a hypothesis of the disease and a measurable concept of interest, wherein the one or more components of the instrumentation asset comprise a measurement method, raw data, and a machine learning algorithm, and wherein the one or more components of the evidence asset comprise a technical validation, an analytical validation, and a clinical validation. In various embodiments, the plurality of components of the measurement stack are a plurality of layers.
In various embodiments, the disease is dementia. In various embodiments, the hypothesis comprises an intervention that slows progression of dementia. In various embodiments, the measurable concept of interest comprises one or more of attention, language, or executive functioning. In various embodiments, the measurement method comprises a method for capturing speech. In various embodiments, the raw data comprises raw speech data captured from a subject or magnetic resonance imaging data. In various embodiments, the machine learning algorithm comprises one or more of a natural language processing algorithm, a clustering algorithm, and a clinical variable predictor. In various embodiments, the analytical validation comprises evidence validating performance of the machine learning algorithm. In various embodiments, the clinical interpretation comprises evidence supporting characterization of the disease according to a clinical dementia rating. In various embodiments, the disease is Parkinson's Disease. In various embodiments, the hypothesis comprises an intervention that reduces tremor. In various embodiments, the measurable concept of interest comprises ability to perform daily activities of moderate intensity. In various embodiments, the measurement method comprises methods for capturing physiological data using a biosensor. In various embodiments, the raw data comprises raw physiological data captured using a biosensor. In various embodiments, the machine learning algorithm transforms the raw data to the dataset. In various embodiments, the analytical validation comprises evidence validating performance of the machine learning algorithm. In various embodiments, the clinical interpretation comprises evidence supporting characterization of the disease in a Parkinson's Disease patient population.
In various embodiments, the disease is Atopic Dermatitis. In various embodiments, the hypothesis comprises an intervention that reduces nocturnal scratch. In various embodiments, the measurable concept of interest comprises nocturnal scratching. In various embodiments, the measurement method comprises methods for capturing physiological data using a wearable device. In various embodiments, the raw data comprises raw physiological data captured using the wearable device. In various embodiments, the machine learning algorithm transforms the raw data to the dataset. In various embodiments, the clinical validation comprises evidence supporting treatments effects on nocturnal scratching in an atopic dermatitis population.
In various embodiments, the disease is Pulmonary Arterial Hypertension. In various embodiments, the hypothesis comprises an intervention that improves patient ability to perform physical activities following treatment. In various embodiments, the measurable concept of interest comprises a performance of daily activities by patients affected by pulmonary arterial hypertension. In various embodiments, the measurement method comprises a wrist-worn device for capturing physiological data. In various embodiments, the raw data comprises raw physiological data measured from sensors of the wrist-worn device, wherein the sensors comprise one or more of an accelerometer, gyroscope, and magnetometer. In various embodiments, the machine learning algorithm transforms the raw data to the dataset. In various embodiments, the clinical validation comprises evidence of improvement in daily performance following an intervention.
Additionally disclosed herein is a non-transitory computer readable medium for characterizing a disease of a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: obtain a measurement of interest from the subject; select a digital measurement solution from a plurality of digital measurement solutions, wherein the plurality of digital measurement solutions are of a common class that is represented by a target solution profile; and apply the selected digital measurement solution to the obtained measurement of interest to characterize the disease for the subject, wherein the digital measurement solution comprises: a measurement definition defining one or more concepts of interest relevant to the disease; an instrumentation asset that transforms the measurement of interest captured according to the measurement definition to a dataset that is informative for characterizing the disease, wherein the instrumentation asset of the digital measurement solution is specific for a device used to capture the measurement of interest; and optionally, an evidence asset for performing one or more validations on the dataset generated by the instrumentation asset, wherein the target solution profile is unchanged over time and enables efficient life-cycle management of the plurality of digital measurement solutions. In various embodiments, the target solution profile represents a generalization of the plurality of digital measurement solutions, wherein an instrumentation asset of the target solution profile is device technology agnostic.
In various embodiments, the instructions that cause the processor to perform the one or more validations further comprises instructions that, when executed by the processor, cause the processor to: perform one or more of a technical validation, an analytical validation, or a clinical validation. In various embodiments, the instructions that cause the processor to perform the technical validation further comprises instructions that, when executed by the processor, cause the processor to compare the dataset generated by the instrumentation asset to specifications of one or more devices used to capture the measurement of interest. In various embodiments, the instructions that cause the processor to perform the analytical validation further comprises instructions that, when executed by the processor, cause the processor to perform any of reliability, specificity, or sensitivity metrics for the dataset; and compare the reliability, specificity, or sensitivity metrics to a threshold value. In various embodiments, the instructions that cause the processor to perform the clinical validation further comprises instructions that, when executed by the processor, cause the processor to assess treatment effects on measurements of interest for the disease. In various embodiments, the digital measurement solution is previously validated by implementing one or more qualification protocols used to establish comparability of solutions across the digital measurement solutions of the target solution profile. In various embodiments, a qualification protocol comprises steps of: a) recruiting a N member participant group; b) capturing measurements of interest across the N member participant group according to a specification of the digital measurement solution; c) transforming the measurements of interest into a dataset according to the specification; and d) validating the dataset to determine whether the digital measurement solution achieves comparable solutions of the target solution profile. In various embodiments, validating the dataset comprises: determining whether a characteristic of the dataset satisfies a threshold value of the target solution profile; and responsive to the determination that the characteristic of the dataset satisfies the threshold value, validating the digital measurement solution as achieving comparability of solutions. In various embodiments, validating the dataset further comprises responsive to determining that the digital measurement solution achieves comparability of solutions, storing an indication of a successful validation in metadata of the digital measurement solution. In various embodiments, the metadata of the digital measurement solution is stored in a catalog accessible for inspection by third party users. In various embodiments, the specification of the digital measurement solution represents an upgraded capability in comparison to a prior version of the digital measurement solution. In various embodiments, the specification of the digital measurement solution represents an upgraded capability included in a newly released device used to capture the measurement of interest. In various embodiments, the upgraded capability is one of an upgraded battery, upgraded data storage, upgraded acquisition frequency, or upgraded data collection algorithm. In various embodiments, the common class of the plurality of digital measurement solutions represents a common method of measuring activity from an individual. In various embodiments, the common method of measuring activity uses a class of devices comprising one or more of wearable devices, devices including accelerometers, devices including gyroscopes, ingestibles, image and voice based devices, touchless sensors, and sensor based smart devices (e.g., scales, thermometers, and respirators). In various embodiments, the instrumentation asset comprises a machine learning algorithm that transforms data captured according to the measurement definition to the dataset.
Additionally disclosed herein is a non-transitory computer readable medium for building a digital measurement solution for characterizing a disease, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: generate a measurement definition of a target solution profile, the measurement definition defining one or more concepts of interest relevant to the disease; generate or select an instrumentation asset for the target solution profile, the instrumentation asset configured to transform data captured according to the measurement definition to a dataset, the instrumentation asset being device technology agnostic and is thereby interchangeable across different target solution profiles; generate an evidence asset of the target solution profile for performing one or more validations on the dataset generated by the instrumentation asset; generate a digital measurement solution by at least specifying a device for the instrumentation asset of target solution profile, wherein the digital measurement solution is of a common class that is represented by the target solution profile, wherein the target solution profile is unchanged over time and thereby enables efficient life-cycle management of the plurality of digital measurement solutions.
In various embodiments, the one or more concepts of interest relevant to the disease comprise medical measurements of the disease or measurable experiences of individuals suffering from the disease. In various embodiments, device technology agnostic comprises one or both of being device-agnostic and being device-version agnostic. In various embodiments, the non-transitory computer readable medium further comprises instructions that, when executed by the processor, cause the processor to implement a qualification protocol to validate the digital measurement solution, the qualification protocol used to establish comparability of solutions across the plurality of digital measurement solutions of the target solution profile. In various embodiments, a qualification protocol comprises steps of: a) recruiting a N member participant group; b) capturing measurements of interest across the N member participant group using a specification of the digital measurement solution; c) transforming the measurements of interest into a dataset according to the specification; and d) validating the dataset to determine whether the digital measurement solution achieves comparable solutions of the target solution profile. In various embodiments, validating the dataset comprises determining whether the dataset satisfies a threshold value of the target solution profile; and responsive to the determination that the dataset satisfies the threshold value, validating the digital measurement solution as achieving comparability of solutions. In various embodiments, validating the dataset further comprises responsive to determining that the digital measurement solution achieves comparability of solutions, storing an indication of a successful validation in metadata of the digital measurement solution. In various embodiments, the metadata of the digital measurement solution is stored in a catalog accessible for inspection by third party users. In various embodiments, the specification of the digital measurement solution represents an upgraded capability of a prior version of the digital measurement solution. In various embodiments, the specification of the digital measurement solution represents an upgraded capability included in a newly released device used to capture the measurement of interest. In various embodiments, the upgraded capability is one of an upgraded battery, upgraded data storage, upgraded acquisition frequency, or upgraded data collection algorithm.
In various embodiments, the measurement definition and evidence asset are fixed for the target solution profile and specific for the disease. In various embodiments, the instrumentation asset of the target solution profile is interchangeable across different target solution profiles for characterizing a same disease or different diseases. In various embodiments, the instrumentation asset is specific for a common method of measuring activity from an individual. In various embodiments, the common method of measuring activity uses a class of devices comprising one or more of wearable devices, devices including accelerometers, devices including gyroscopes, ingestibles, image and voice based devices, touchless sensors, and sensor based smart devices (e.g., scales, thermometers, and respirators). In various embodiments, the digital measurement solution is one of the digital measurement solutions shown in Table 5. In various embodiments, the target solution profile is one of the target solution profiles shown in Table 4. In various embodiments, the disease is a condition shown in Table 1. In various embodiments, the one or more concepts of interest are selected from a concept of interest shown in Table 3.
Additionally disclosed herein is a non-transitory computer readable medium for providing one or more digital measurement solutions useful for characterizing a disease, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: provide a catalogue comprising a plurality of target solution profiles, wherein each of one or more of the target solution profiles comprises: a measurement definition of the target solution profile defining one or more concepts of interest relevant to the disease; an instrumentation asset that transforms a measurement of interest captured according to the measurement definition to a dataset that is informative for characterizing the disease, the instrumentation asset being device technology agnostic and is thereby interchangeable across different target solution profiles; and an evidence asset for performing one or more validations on the dataset generated by the instrumentation asset; receive, from a third party, a selection of one of the target solution profiles; and provide one or more digital measurement solutions useful for characterizing the disease to the third party, wherein the one or more digital measurement solutions are of a common class represented by the selected target solution profile. In various embodiments, non-transitory computer readable media disclosed herein further comprise instructions that, when executed by a processor, cause the processor to: receive, from the third party, a search query; for each of the one or more target solution profiles in the plurality of target solution profiles, evaluate the target solution profile to determine whether the target solution profile satisfies the query; and return a list of target solution profiles that satisfy the query. In various embodiments, the instructions that cause the processor to evaluate the target solution profile further comprises instructions that, when executed by the processor, cause the processor to evaluate one or more components of the measurement definition for a concept of interest that satisfies the query. In various embodiments, non-transitory computer readable media disclosed herein further comprise instructions that, when executed by a processor, cause the processor to replace an instrumentation asset of one of the one or more digital measurement solutions with a second instrumentation asset to generate a revised digital measurement solution; and provide the revised digital measurement solution to the third party.
In various embodiments, non-transitory computer readable media disclosed herein, further comprise instructions that, when executed by a processor, cause the processor to: receive, from a third party, a suggested target solution profile not present in the provided catalog; and further generate the suggested target solution profile for inclusion in the catalog.
In various embodiments, the target solution profile comprises a measurement stack comprising a plurality of components. In various embodiments, each of the measurement definition, the instrumentation asset, and the evidence asset are represented by one or more components in the plurality of components. In various embodiments, an order of the plurality of components comprises one or more components of the measurement definition, followed by one or more components of the instrumentation asset, and further followed by one or more components of the evidence asset. In various embodiments, a component of the measurement definition interfaces with a component of the instrumentation asset, and a component of the instrumentation asset interfaces with the evidence asset. In various embodiments, the one or more components of the measurement definition comprise one or more of a hypothesis of the disease and a measurable concept of interest. In various embodiments, the one or more components of the instrumentation asset comprise one or more of a measurement method, raw data, and a machine learning algorithm. In various embodiments, the one or more components of the evidence asset comprise one or more of a technical validation, an analytical validation, and a clinical validation. In various embodiments, the one or more components of the measurement definition comprise a hypothesis of the disease and a measurable concept of interest, wherein the one or more components of the instrumentation asset comprise a measurement method, raw data, and a machine learning algorithm, and wherein the one or more components of the evidence asset comprise a technical validation, an analytical validation, and a clinical validation. In various embodiments, the plurality of components of the measurement stack are a plurality of layers.
In various embodiments, the disease is dementia. In various embodiments, the hypothesis comprises an intervention that slows progression of dementia. In various embodiments, the measurable concept of interest comprises one or more of attention, language, or executive functioning. In various embodiments, the measurement method comprises a method for capturing speech. In various embodiments, the raw data comprises raw speech data captured from a subject or magnetic resonance imaging data. In various embodiments, the machine learning algorithm comprises one or more of a natural language processing algorithm, a clustering algorithm, and a clinical variable predictor. In various embodiments, the analytical validation comprises evidence validating performance of the machine learning algorithm. In various embodiments, the clinical interpretation comprises evidence supporting characterization of the disease according to a clinical dementia rating.
In various embodiments, the disease is Parkinson's Disease. In various embodiments, the hypothesis comprises an intervention that reduces tremor. In various embodiments, the measurable concept of interest comprises ability to perform daily activities of moderate intensity. In various embodiments, the measurement method comprises methods for capturing physiological data using a biosensor. In various embodiments, the raw data comprises raw physiological data captured using a biosensor. In various embodiments, the machine learning algorithm transforms the raw data to the dataset. In various embodiments, the analytical validation comprises evidence validating performance of the machine learning algorithm. In various embodiments, the clinical interpretation comprises evidence supporting characterization of the disease in a Parkinson's Disease patient population
In various embodiments, the disease is Atopic Dermatitis. In various embodiments, the hypothesis comprises an intervention that reduces nocturnal scratch. In various embodiments, the measurable concept of interest comprises nocturnal scratching. In various embodiments, the measurement method comprises methods for capturing physiological data using a wearable device. In various embodiments, the raw data comprises raw physiological data captured using the wearable device. In various embodiments, the machine learning algorithm transforms the raw data to the dataset. In various embodiments, the clinical validation comprises evidence supporting treatments effects on nocturnal scratching in an atopic dermatitis population.
In various embodiments, the disease is Pulmonary Arterial Hypertension. In various embodiments, the hypothesis comprises an intervention that improves patient ability to perform physical activities following treatment. In various embodiments, the measurable concept of interest comprises a performance of daily activities by patients affected by pulmonary arterial hypertension. In various embodiments, the measurement method comprises a wrist-worn device for capturing physiological data. In various embodiments, the raw data comprises raw physiological data measured from sensors of the wrist-worn device, wherein the sensors comprise one or more of an accelerometer, gyroscope, and magnetometer. In various embodiments, the machine learning algorithm transforms the raw data to the dataset. In various embodiments, the clinical validation comprises evidence of improvement in daily performance following an intervention.
These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description and accompanying drawings. It is noted that wherever practicable similar or like reference numbers may be used in the figures and may indicate similar or like functionality. For example, a letter after a reference numeral, such as “third party entity 110A,” indicates that the text refers specifically to the element having that particular reference numeral. A reference numeral in the text without a following letter, such as “third party entity 110,” refers to any or all of the elements in the figures bearing that reference numeral (e.g., “third party entity 110” in the text refers to reference numerals “third party entity 110A” and/or “third party entity 110A” in the figures).
Terms used in the claims and specification are defined as set forth below unless otherwise specified.
The term “subject” or “patient” are used interchangeably and encompass a cell, tissue, organism, human or non-human, mammal or non-mammal, male or female, whether in vivo, ex vivo, or in vitro.
The term “disease” or “condition” are used interchangeably and generally refer to a diseased status of a subject. Generally, a standardized solution, such as a digital measurement solution, is implemented to characterize the disease for the subject.
The phrase “measurement stack” refers to an organization of one or more assets that are composed of components. In particular embodiments, the measurement stack is composed of two or more assets. In particular embodiments, the measurement stack is composed of three or more assets. For example, the measurement stack includes a measurement definition asset, an instrumentation asset, and an evidence asset. The measurement stack provides a structure for standardized solutions, such as a target solution profile or a digital measurement solution.
The phrases “target solution profile” or “TSP” refer to a measurement stack in which generic descriptions are incorporated to provide a device technology agnostic profile (e.g., a profile that is independent of a particular hardware device and/or independent of particular software). In various embodiments, a target solution profile includes each of a measurement definition asset, an instrumentation asset, and an evidence asset. In various embodiments, the instrumentation asset of the target solution profile describes general methods of capturing and transforming raw data of interest but does not specify particular devices or algorithms for capturing and transforming the raw data. Target solution profiles represent a common class of digital measurement solutions. Target solution profiles may specify performance requirements and/or standards such that digital measurement solutions of the common class represented by the target solution profile are evaluated and confirmed to perform within the performance requirements and/or standards.
The phrases “digital measurement solution” or “DMS” refer to a specific digital solution built upon a measurement stack. In various embodiments, a DMS specifies all of the components of a full solution, which can include devices, algorithms, external data, definition, and/or evidence. For example, a digital measurement solution identifies specific devices or software for capturing raw data. In various embodiments, a digital measurement solution identifies a specific algorithm for transforming the raw data into meaningful health data. Thus, implementation of a digital measurement solution is useful for characterizing a disease for a subject.
The phrase “standardized solution” refers to standard digital solutions useful for characterizing disease. Examples of standardized solutions include digital measurement solutions and target solution profiles.
Generally, the digital solution system 130 builds and/or maintains standardized solutions, examples of which include digital measurement solutions (DMSs) and target solution profiles (TSPs), that are built on measurement stacks. Standardized solutions are useful for characterizing diseases for subjects and furthermore, enables efficient life cycle management of the various solutions. In various embodiments, the digital solution system 130 interacts with various third party entities (e.g., third party entities 110A and/or 110B) to build and maintain DMSs and TSPs. In various embodiments, the digital solution system 130 represents a centralized marketplace incorporating these standardized DMSs and TSPs. Thus, the digital solution system 130 provides standardized DMSs and TSPs to third party entities (e.g., third party entities 110A and/or 110B) via the centralized marketplace such that the third party entities can use the standardized solutions e.g., for characterize a disease or condition.
In various embodiments, the third party entity 110 represents a partner entity of the digital solution system 130. In some embodiments, the third party entity 110 is a partner entity that collaborates with the digital solution system 130 for building TSPs and/or DMSs. In some scenarios, the third party entity 110 represents an asset developer. As one example, the third party entity 110 can develop components that can be provided to the digital solution system 130 for incorporation into standardized solutions (e.g., DMSs or TSPs). As another example, the third party entity 110 can provide feedback to the digital solution system 130. For example, the third party entity 110 can provide suggestions as to valuable standardized solutions (e.g., DMSs or TSPs). These standardized solutions may be currently missing (e.g., not present in the catalog or available in the marketplace). Thus, the digital solution system 130 can generate these suggested standardized solutions, perform the appropriate validation, and include them in the marketplace.
In some embodiments, the third party entity 110 represents a regulatory specialist. Here, the third party entity 110 can interact with the digital solution system 130 to verify the standardized solutions (e.g., DMSs and TSPs) and approve them as standard solutions for clinical trials. As described in further detail herein, DMSs and TSPs may include components that provide specific guidelines for regulatory specialists, which can lead to improved standardization and adoption of these solutions.
In various embodiments, multiple third party entities 110 collaborate together to build standardized solutions and to achieve regulatory acceptance of the standardized solutions. For example, the multiple third party entities 110 collaborate together to enable dynamic regulatory assessment of standardized solutions (e.g., DMSs and/or TSPs). In various embodiments, the multiple third party entities 110 includes stakeholders who are interested in building the standardized solutions. Such stakeholders can include asset developers (e.g., entities that build and/or provide components and/or assets), pharmaceutical companies, observers, service providers, and/or customers (e.g, entities interested in using standardized solutions). Thus, these stakeholders can provide feedback in working together to build the standardized solutions. In various embodiments, the multiple third party entities 110 further includes regulatory individuals who perform the regulatory assessment of the standardized solutions. Thus, the regulatory individuals can provide regulatory acceptance of standardized solutions. In various embodiments, the regulatory individuals can interact with other multiple third party entities 110 (e.g., stakeholders) to enable dynamic regulatory assessment. For example, regulatory individuals can correspond with stakeholders in understanding the context and use cases of the standardized solutions, thereby ensuring more rapid regulatory approval.
In some embodiments, the third party entity 110 represents a customer who is interested in accessing and using the standardized solutions, such as DMSs, to characterize diseases for subjects. Example customers include any of a sponsor (e.g., clinical trial sponsor), a clinical researcher, a health care specialist, a physician, a vendor, or a supplier. In such embodiments, the third party entity 110 can interact with the digital solution system 130 to access and use the standardized solutions. For example, the digital solution system 130 may provide TSPs and/or DMSs to the third party entity 110 that suits the needs of the third party entity 110. For example, as described in further detail herein, DMSs and TSPs may identify particular specifications (e.g., device specifications or software specifications) that establish the measurements of interest that are captured for a particular disease or condition, e.g., captured from subjects with or without the disease or condition. Thus, a third party entity 110 who is interested in characterizing the particular disease or condition can evaluate the required specifications and identify the appropriate DMSs or TSPs that best suit their need. The digital solution system 130 can provide the appropriate DMSs or TSPs. Using the appropriate DMS, the third party entity 110 characterizes a disease for one or more subjects. For example, the third party entity 110 can capture a measurement of interest from a subject according to measurement methods described in a DMS. The third party entity 110 can further transform the measurement of interest into meaningful health data using an algorithm specified in the DMS. Then, the third party entity 110 interprets the meaningful health data and characterizes the disease.
This disclosure contemplates any suitable network 120 that enables connection between the digital solution system 130 and third party entities 702. The network 120 may comprise any combination of local area and/or wide area networks, using both wired and/or wireless communication systems. In one embodiment, the network 120 uses standard communications technologies and/or protocols. For example, the network 120 includes communication links using technologies such as Ethernet, 802.11, worldwide interoperability for microwave access (WiMAX), 3G, 4G, code division multiple access (CDMA), digital subscriber line (DSL), etc. Examples of networking protocols used for communicating via the network 120 include multiprotocol label switching (MPLS), transmission control protocol/Internet protocol (TCP/IP), hypertext transport protocol (HTTP), simple mail transfer protocol (SMTP), and file transfer protocol (FTP). Data exchanged over the network 120 may be represented using any suitable format, such as hypertext markup language (HTML) or extensible markup language (XML). In some embodiments, all or some of the communication links of the network 120 may be encrypted using any suitable technique or techniques.
Referring first to the asset module 140, it generates or obtains individual components and constructs assets composed of two or more components. The asset module 140 may store components and/or assets in the component store 170. Examples of components include 1) an aspect of health component relevant to the disease, 2) a hypothesis component, 3) a concept of interest component which defines a measurable unit that informs the aspect of health of the disease, 4) a measurement method component that defines how raw data is captured, 5) a raw data component specifying characteristics of the raw data, 6) an algorithm component for implementing an algorithm that transforms the raw data, 7) a health data component describing meaningful interpretation of data relevant for the disease, 8) an analytical validation component, 9) clinical validation component, and 10) a regulatory intelligence component. Further description of these example components is included herein.
In various embodiments, the asset module 140 may organize individual component into assets that are composed of two or more components. As an example, the asset module 140 may organize A) an aspect of health component relevant to the disease, B) a hypothesis component, and C) a concept of interest component into an asset, hereafter referred to as a measurement definition asset. As another example, the asset module 140 can organize A) a measurement method component that defines how raw data is captured, B) raw data component specifying characteristics of the raw data, C) algorithm component for implementing an algorithm that transforms the raw data, and D) health data component describing meaningful interpretation of data relevant for the disease into an asset, hereafter referred to as an instrumentation asset. As yet another example, the asset module 140 can organize A) analytical validation component, B) clinical validation component, and C) regulatory intelligence component into an asset, hereafter referred to as an evidence asset. Further details of these example assets are described herein.
In various embodiments, the asset module 140 generates components and constructs assets through de novo methods. For example, the asset module 140 identifies a particular disease and generates components and constructs assets that are useful for characterizing the particular disease. In various embodiments, the asset module 140 may receive components and/or assets from third party entities (e.g., third party entities 110 shown in
The target solution profile module 145 generates target solution profiles (TSPs) using components and/or assets e.g., components and/or assets generated de novo by the asset module 140 or components and/or assets obtained by the asset module 140 from third party entities. In various embodiments, a TSP includes a measurement definition asset, instrumentation asset, and/or evidence asset. In particular embodiments, a TSP includes each of a measurement definition asset, instrumentation asset, and evidence asset. Generally, a TSP represents a measurement stack in which generic descriptions are incorporated to provide a device technology agnostic profile (e.g., a profile that is independent of a specific hardware device and independent of specific software). The generic descriptions are valuable to ensure that assets of the TSP can be readily interchangeable. For example, the instrumentation asset of a TSP can specify a class of devices for capturing measurements. Examples of a class of devices include, but are not limited to: wearable devices (e.g., wrist-worn device), ingestibles, image and voice based devices, touchless sensors, and sensor based smart devices (e.g., scales, thermometers, and respirators).
In various embodiments, the TSP module 145 builds a TSP using a condition-focused approach (e.g., bottom-up approach). Here, the TSP is built by first identifying a condition or disease of interest. Thus, the components of the TSP are assembled for the purpose of characterizing the disease of interest. In various embodiments, the TSP module 145 builds a TSP an instrumentation-focused approach (e.g., top-down approach). Here, the TSP is built by identifying the components and assets that are available for use (e.g., components and assets stored in component store 170). This ensures that components and assets that have previously been generated and/or validated can be easily repurposed. Thus, in various embodiments, building a TSP can involve repurposing components and assets from other TSPs such that new components and assets need not be generated. In particular embodiments, instrumentation assets of other TSPs can be repurposed for building a new TSP, even in scenarios where the other TSPs and the new TSP are developed for different diseases. The TSP module 145 can store the generated TSPs in the TSP store 175. Further details of example TSPs are described herein.
The digital measurement solution (DMS) module 150 builds one or more DMSs. In various embodiments, the DMS module 150 builds one or more DMSs by incorporating specific information into a TSP. Here, the TSP represents a class of solutions for the one or more DMSs. For example, the DMS module 150 can incorporate specific device hardware into a component of a TSP. Thus, a DMS specifies the particular device that is to be used to capture raw measurements. As another example, the DMS module 150 can incorporate specific algorithms into a component of a TSP. Thus, a DMS specifies the particular algorithm that is used to transform raw measurements into a meaningful health dataset that can be interpreted to characterize the disease.
In various embodiments, the DMS module 150 builds two or more DMSs of a common class represented by a TSP. In various embodiments, the DMS module 150 builds three or more DMSs, four or more DMSs, five or more DMSs, six or more DMSs, seven or more DMSs, eight or more DMSs, nine or more DMSs, ten or more DMSs, eleven or more DMSs, twelve or more DMSs, thirteen or more DMSs, fourteen or more DMSs, fifteen or more DMSs, sixteen or more DMSs, seventeen or more DMSs, eighteen or more DMSs, nineteen or more DMSs, twenty or more DMSs, twenty five or more DMSs, fifty or more DMSs, a hundred or more DMSs, two hundred or more DMSs, three hundred or more DMSs, four hundred or more DMSs, five hundred or more DMSs, six hundred or more DMSs, seven hundred or more DMSs, eight hundred or more DMSs, nine hundred or more DMSs, or a thousand or more DMSs of a common class represented by a TSP. The DMS module 150 can store the generated DMSs in the DMS store 180. Further details of example DMSs are described herein.
The qualification protocol module 155 performs qualification protocols that enable rapid onboarding of upgraded DMSs (e.g., in view of upgraded devices and/or upgraded software releases) by validating comparability of results across multiple DMSs of a common class. For example, when a new device or software package is released, the new device or software package can be incorporated in an updated or upgraded DMS. Here, the qualification protocol module 155 implements a qualification protocol to validate the new DMS incorporating the new device or new software package. This ensures that the new DMS achieves comparable results to other DMSs of the same common class. Further details of the implementation of qualification protocols are described herein.
In various embodiments, DMSs that have undergone successful validation using a qualification protocol can be identified as successfully validated. For example, metadata associated with a successfully validated DMS can be annotated. For example, the metadata can identify the qualification protocol that was used, as well as the fact that the DMS was successfully validated. In various embodiments, the metadata including the annotation can be available for inspection by a third party. Therefore, a third party, such as a customer who is interested in using a DMS to characterize a disease, can select a DMS that has been successfully validated.
The disease characterization module 160 implements a DMS to characterize a disease. In various embodiments, the disease characterization module 160 can be employed by a third party entity (e.g., third party entity 110 shown in
The marketplace module 165 implements a marketplace of the standardized solutions (e.g., DMSs and TSPs) and enables third party entities to access the DMSs and TSPs for their uses. In various embodiments, the marketplace module 165 provides an interface to third party entities that depicts the various DMSs and TSPs that are available for access. Such an interface can be organized as a catalog for ease of access.
In various embodiments, the marketplace module 165 provides a catalog of TSPs that are useful for characterizing various diseases. The marketplace module 165 may receive a selection of one of the TSPs. For example, a third party may select a TSP for characterizing a disease that is of interest for the third party. Furthermore, the third party may select the TSP because it includes specifications that align with the capabilities of the third party. In one scenario, the marketplace module 165 can provide the selected TSP to the third party. In one scenario, the marketplace module 165 can identify one or more DMSs that are of a common class represented by the selected TSP. Here, the marketplace module 165 provides the one or more DMSs of the common class to the third party.
In various embodiments, the marketplace module 165 may provide recommendations to third parties that are accessing the marketplace. For example, the marketplace module 165 can provide a recommendation identifying one or more components, one or more assets, one or more TSPs, or one or more DMSs to a third party. This can be useful for third parties that may need additional guidance as to the best standardized solution that will fit their needs.
In various embodiments, the marketplace module 165 receives suggestions as to additional standardized solutions that would be of value. For example, the marketplace module 165 may receive a suggestion from a third party for a particular DMS or TSP that is not present in the marketplace. Such a third party may be an asset developer or a customer who identifies a gap that is not satisfied by the current offerings of standardized solutions. For example, the suggestion may identify that specifications of a particular device exceed the specifications of available TSPs and DMSs. Therefore, the marketplace module 165 can provide the suggestion to any of the asset module 140, TSP module 145, and/or DMS module 150 to generate additional standardized solutions that can be included in the marketplace.
In various embodiments, the marketplace module 165 provides a catalog of target solution profiles and receives a search query. For example, a third party presented with the catalog of target solution profiles my provide a search query for a particular component or asset in a target solution profile. In various embodiments, the third party provides a search query for a concept of interest or for a particular disease. The marketplace module 165 queries the available TSPs (e.g., TSPs stored in the target solution profile store 175) according to the search query, and returns a list of TSPs that satisfy the search query. For example, if the search query specifies a particular concept of interest the marketplace module 165 evaluates the components of the TSPs for a concept of interest that satisfies the search query. Thus, the marketplace module 165 can provide the list of TSPs that satisfy the search query (e.g., to the third party).
Embodiments disclosed herein involve the building of TSPs and DMSs, as well as the implementation of TSPs and DMSs for characterizing disease. Generally, TSPs and DMSs are built on a measurement stack comprised of one or more components (also referred to herein as layers). Namely, a measurement stack provides a structure or framework for a TSP or DMS. The components and/or assets of a measurement stack can be generated and/or maintained by the asset module 140, as described above in reference to
The goal of the measurement stack is to fulfill the earlier mentioned gaps as, for example, the lack of standardization and concerns about the collection, analysis, and interpretation of data. First, the measurement stack provides a standardized structure that represents a universal way of describing a solution, thereby allowing for standardization. Second, the measurement stack initiates and allows for harmonization between multiple assets and components. Third, the measurement stack model will enable assets to transition between diseases and use-cases, enabling component level reusability.
In various embodiments, the measurement stack includes one or more assets. Examples of assets include a measurement definition asset, an instrumentation asset, or an evidence asset. An asset refers to one or more components of the stack. In various embodiments, an asset refers to two or more components. In various embodiments, an asset refers to three or more components. In various embodiments, an asset refers to three or more components.
In various embodiments, the measurement definition asset includes two components. In various embodiments, the measurement definition asset includes three components. In various embodiments, the measurement definition asset includes four components. In various embodiments, the instrumentation asset includes two components. In various embodiments, the instrumentation asset includes three components. In various embodiments, the instrumentation asset includes four components. In various embodiments, the evidence asset includes two components. In various embodiments, the evidence asset includes three components. In various embodiments, the evidence asset includes four components.
In various embodiments, the measurement stack includes two assets. For example, the measurement stack may include a measurement definition asset related to a particular disease and an instrumentation asset that describes the capturing of data that is useful for characterizing the condition. In particular embodiments, the measurement stack includes three assets. For example, the measurement stack may include a measurement definition asset related to a particular disease, an instrumentation asset that describes the capturing of data that is useful for characterizing the disease, and an evidence asset for validating meaningful datasets of the disease.
In various embodiments, the components of an asset are connected to one another. For example, the components of an asset are configured to communicate with at least one another component of the same asset. For example, within an asset, the components are organized as layers, and therefore, a first component is configured to communicate with a second component that is adjacent to the first component. This enables the transfer of information from one component to the next component.
In various embodiments, a component of a first asset is connected to a component of a second asset. Thus, the component of the first asset can communicate with the component of the second asset. As an example, within a measurement stack, a first asset may be located lower in the measurement stack in relation to a second asset. Here, a component of the first asset can be connected to a component of the second asset, thereby enabling the first asset and second asset to interface with each other.
In various embodiments, the assets of the measurement stack are ordered as follows (from bottom to top of the stack): 1) measurement definition asset and 2) instrumentation asset. In particular embodiments, the assets of the measurement stack are ordered as follows (from bottom to top of the stack): 1) measurement definition asset, 2) instrumentation asset, and evidence asset.
Reference is now made to
As shown in
Generally, the measurement definition asset defines measurable concepts related to the disease. Thus, the measurable concept is informative for characterizing a disease (e.g., presence of a disease, severity of a disease, progression of a disease, etc.). For example, for a condition of atopic dermatitis, the measurement definition asset may define a concept related to atopic dermatitis to be nocturnal scratching. Thus, nocturnal scratching can be measured for a subject to characterize the disease for the subject (e.g., higher quantity of nocturnal scratching can be indicative of more severe atopic dermatitis as opposed to lower quantity of nocturnal scratching). Examples of individual components of the measurement definition asset are described in further detail herein.
The instrumentation asset defines how the measurement concepts related to the disease are captured, and further defines how the captured raw data is transformed into an interpretable, meaningful health dataset. For example, the instrumentation asset can describe device specifications that influence the capture of the raw data. Furthermore, the instrumentation asset can transform the raw data into the health dataset that is more meaningful for the particular disease. The meaningful health dataset can be measurements of the concept related to the disease (as described in relation to the measurement definition asset) or can be readily interpreted to obtain measurements of the concept related to the disease. Returning to the atopic dermatitis example above, the meaningful health dataset can include a measure of nocturnal scratching (e.g., scratching events per hour, scratching duration per hour, total number of scratching events). Alternatively, the meaningful health dataset can be a dataset from which the measure of nocturnal scratching (e.g., scratching events per hour, scratching duration per hour, total number of scratching events) can be readily extracted. Examples of individual components of the instrumentation asset are described in further detail herein.
The evidence asset includes one or more validations that validate the dataset generated by the instrumentation asset. This ensures that the dataset (e.g., health dataset) generated by the instrumentation asset is accurate and can be used to accurately characterize the disease. Examples of validations included in the evidence asset can include technical validations, analytical validations, and/or clinical validations. In various embodiments, the evidence asset includes two or more validations. In various embodiments, the evidence asset includes three or more validations. Generally, performing the validations of the evidence asset ensures that measurements are accurate, and therefore, can be recognized as eligible (e.g., as a standard) for clinical trial use and approval. Examples of individual components of the evidence asset are described in further detail herein.
In various embodiments, the different assets of the measurement stack are selected or generated specifically for the particular condition. For example, the measurement definition asset may describe concepts particularly relevant to the condition, and therefore, the measurement definition asset may be specific for the condition. In some embodiments, the different assets in a measurement stack are interchangeable and can be used for measurement stacks of various diseases. For example, the instrumentation asset can be interchangeable, such that the instrumentation asset can be included in a first measurement stack for a first condition, and can further be included in a second measurement stack for a second condition. As such, interchangeable or reusable assets enables the more efficient generation and building of measurement stacks.
In various embodiments, the measurement stack can be differently arranged such that additional or fewer components are included. In various embodiments, although not shown, the measurement stack can further include a regulatory component, which is valuable for aligning regulatory experts with the measurement endpoints. Such a regulatory component may be included in the evidence asset. Thus, in such embodiments, there are a total of 9 components in the measurement stack.
In various embodiments, functionalities of two or more components in an asset can be combined into a single component. Thus, there may be fewer components in the measurement stack than the 8 components that are explicitly shown in
Referring first to the condition (e.g., physical or medical condition shown in
The meaningful aspect of health (MAH) (referred to as aspect of health in
The concept of interest describes a measurable unit that informs the meaningful aspect of health. For example, the concept of interest is a measurable unit that can be used to inform the meaningful aspect of health relevant to the disease, which therefore informs the severity of the disease. Examples of concepts of interest (COI) are shown in Table 3. In various embodiments, the concept of interest describes a medical measurement of the disease (e.g., a measurable unit that the health care community would measure for determining severity of the disease). For example, in the context of Parkinson's disease, a medical measurement of Parkinson's is tremors. Here, the quantity of tremors can be a measure of the severity of the disease. In various embodiments, the concept of interest describes a measurable experience of individuals suffering from the disease. Here, the measurable experience may not be the medically relevant measurement unit, but may nonetheless have significant impact on patients afflicted with the disease. In such embodiments, the concept of interest can be a symptom of the disease that the patient would like to modify. For example, again in the context of Parkinson's disease, a measurable experience for individuals suffering from Parkinson's may be sleep deprivation. Although sleep deprivation is not the medical measurement unit of Parkinson's Disease, it is nonetheless a measure that can be informative of the severity of the disease.
Referring next to the measurement method component, it generally describes the solutions that are implemented for capturing data of the concept of interest. For example, solutions of the measurement method component include hardware, software, or firmware solutions. Example solutions of the measurement method component include sensors, devices such as computational devices, cellular devices or wearables, as well as mobile applications. In various embodiments, sensors can be built into devices, such as a wearable device or a cellular device.
In various embodiments, the measurement method component identifies the specifications of the measurement method. For example, for a wearable device, the measurement method component identifies the operating specifications of the wearable device (e.g., frequency or a frequency range at which the device captures data (e.g., 10-100 Hz), time intervals during which the device captures data (e.g., 24 hours a day, or in response to a command), presence of one or more sensors of the wearable device that capture data, storage capacity of the wearable device, and/or estimated battery life). In various embodiments, the measurement method employs products that process data captured by (mobile) sensors using algorithms to generate measures of behavioral and/or physiological function. This includes novel measures and indices of characteristics for which the underlying biological processes are not yet understood. Like other digital medicine products, these may be characterized by a body of evidence to support their quality, safety, and effectiveness as indicated in their performance requirements.
Referring next to the raw data, this component represents the raw datasets which are captured according to the particular methods of the measurement method component. For example, if the measurement method component identifies a wearable device (and the corresponding specifications), the raw data represents the dataset captured by the wearable device according to the specifications. In various embodiments, raw data by itself does not provide for interpretable, meaningful data. As a specific example, a raw file may include data captured at 10-100 Hz accelerations. This is captured in 3D SI units (XYZ g-force) with 28 days of continuous data collection. In short, this example describes what raw data is captured (accelerations in 3D SI units), its frequency (10-100 Hz), and the amount of data that is captured (28 days).
Referring next to the algorithm, it transforms the raw data from the raw data component into meaningful datasets (e.g., meaningful health data relevant for measuring the concept of interest). Returning again to the example of atopic dermatitis, an algorithm interprets raw measurement device data captured during sleep and transforms the raw data into meaningful health data (e.g., scratching events). In some scenarios, an algorithm is specific for a particular measurement method. Therefore, a particular algorithm in the algorithm component can only translate raw dataset outcomes that are captured from a particular measurement method.
Referring next to the health data component, it includes health data, also referred to herein as meaningful health data or meaningful health dataset. The health data is transformed by the algorithm from the raw data and represents an interpretable dataset that is informative for the particular concept of interest. Returning again to the atopic dermatitis example, health data can include, or be readily interpreted to include any of total sleep time, scratching events per hour, and the total number of scratching events. Here, health data is the outcome of algorithms/other processing to convert “raw data” into its final health-related data. One example may include converting accelerometer data into number of steps. There may be intermediary stages of this, for example identifying each episode of severe symptoms during the day could be one step, then a further refinement is the calculation of average time of all of these. Both of those could be classified as health data.
Referring next to the analytical validation component, it involves validating one or more of the other components in the measurement stack. In various embodiments, the input to the analytical validation include the components of the measurement definition asset, and components of the instrumentation asset. The output of the analytical validation includes supporting evidence of a successful or failed validation of the corresponding solution incorporating the components of the measurement definition asset and components of the instrumentation asset. Generally, a digital measurement solution is incomplete unless the results it generates are proven to be analytically valid to support clinical interpretation. During the analytical validation, a digital measurement solution is exposed to a series of test conditions and procedural stress to generate sample data and the results are documented for statistical analysis. The results either validate or redefine the functional range outside of which the reliability of measurements may be questionable. A successful analytical validation would mean solutions that fit the profile can support precise labelling claims without unanticipated risks or consequences.
In various embodiments, the analytical validation component may perform an analytical validation of device specifications, algorithms, and health data output. In various embodiments, the analytical validation involves comparing data to an appropriate measurement standard. Example measurement standards for various diseases can be established by third parties or in the community. For example, example measurement standards for different diseases can be standards established by ICHOM Conect.
For example, analytical validation ensures that the meaningful health data meets requisite sensitivity, specificity, and/or reliability requirements. In various embodiments, the requisite sensitivity requirements is any of at least 50% sensitivity, at least 60% sensitivity, at least 70% sensitivity, at least 75% sensitivity, at least 80% sensitivity, at least 85% sensitivity, at least 90% sensitivity, at least 91% sensitivity, at least 92% sensitivity, at least 93% sensitivity, at least 94% sensitivity, at least 95% sensitivity, at least 96% sensitivity, at least 97% sensitivity, at least 98% sensitivity, or at least 99% sensitivity. In various embodiments, the requisite specificity requirements is any of at least 50% specificity, at least 60% specificity, at least 70% specificity, at least 75% specificity, at least 80% specificity, at least 85% specificity, at least 90% specificity, at least 91% specificity, at least 92% specificity, at least 93% specificity, at least 94% specificity, at least 95% specificity, at least 96% specificity, at least 97% specificity, at least 98% specificity, or at least 99% specificity. In various embodiments, the requisite reliability requirements is any of at least 50% reliability, at least 60% reliability, at least 70% reliability, at least 75% reliability, at least 80% reliability, at least 85% reliability, at least 90% reliability, at least 91% reliability, at least 92% reliability, at least 93% reliability, at least 94% reliability, at least 95% reliability, at least 96% reliability, at least 97% reliability, at least 98% reliability, or at least 99% reliability.
In various embodiments, the analytical validation enables the comparison of the digital solution offered by the measurement stack to a reference measure that is currently employed or was previously developed for characterizing the disease. For example, returning to the example of atopic dermatitis, the analytical validation can establish that the digital solution offered by the measurement stack appropriately measures nocturnal scratching according to appropriate reliability, specificity, and sensitivity requirements. Here, the digital solution can be comparable to, or better than a reference measure (e.g., infrared observation to monitor nocturnal scratching).
In various embodiments, the analytical validation component includes an analytical validation and additionally or alternatively includes a technical validation. In various embodiments, the technical validation verifies that the datasets (e.g., raw data from the raw data component and the health data from the health data component) are appropriate. As an example, the technical validation can evaluate whether the captured raw data is in accordance with firmware and/or software protocols that are specific to the device. As another example, the technical validation can evaluate where the raw data captured by the measurement method is according to the specifications identified in the measurement method. For example, the specifications can include battery life, data storage, available measure frequencies. Therefore, the technical validation determines whether the raw data captured by the measurement method aligns with the specifications. As an example, if the raw dataset indicates that the data was captured at a frequency that exceeds the specifications identified in the measurement method, the technical validation can flag the issue and the validation process fails. Alternatively, if the raw dataset indicates that the data was captured at a frequency that is within the specifications identified in the measurement method, the technical validation can be deemed a success. Further details and examples of technical validations are described in Goldsack, J. C., et al. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs). npj Digit. Med. 3, 55 (2020), which is hereby incorporated by reference in its entirety.
Referring next to the clinical validation component, it involves a clinical validation of the digital solution. Clinical validation is the process that evaluates whether the measurement solution acceptably identifies, measures, or predicts a meaningful clinical, biological, physical, functional state, or experience in the specified context of use. An understanding of what level of accuracy, precision, and reliability is valuable for a solution to be useful in a specific clinical research setting. Clinical validation is intended to take a measurement that has undergone verification and analytical validation steps and evaluate whether it can answer a specific clinical question. Generally, a digital measurement solution is incomplete unless the results it generates are interpretable from a clinical perspective and sufficiently relevant to the meaningful aspects of health for the disease. Here the clinical validation component provides the guidelines to clinically interpret the measurements.
For example, the clinical validation can involve analyzing whether the digital solution identifies, measures, and predicts the meaningful clinical, biological, physical, functional state, or experience relevant for the disease.
As an example of a clinical validation, it may include guidelines identifying that a temperature measurement with a delta of 0.00001% is irrelevant for clinical decision making. Furthermore, it may identify that the standard for temperature is a delta of 0.1 degrees. In various embodiments, clinical validation is an in vivo validation that is performed in a specific target population. Thus, clinical validation represents a check as to whether the measurement stack is valid to answer clinical questions relevant to the disease. Returning again to the example of atopic dermatitis, the clinical validation can involve assessing the treatment effects of an intervention on nocturnal scratching within a patient population. Here, the intervention is expected to reduce the quantity of nocturnal scratching. The specific procedure identified in the clinical validation can be performed during or after the clinical trial to ensure that changes in the patient population are accurately evaluated, which provides an accurate evaluation of the impact of the intervention. Further details of clinical validation is described in Goldsack, J. C., et al. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs). npj Digit. Med. 3, 55 (2020), which is hereby incorporated by reference in its entirety.
In various embodiments, the measurement stack further includes a regulatory component for aligning regulatory experts with the measurement endpoints. Generally, the regulatory component may include regulatory guidelines. In various embodiments, the regulatory component includes scientific advice from third parties, such as third party regulators. For example, for a “nocturnal scratch” measure, there is a need for standardizing what “nocturnal” refers to. Thus, the regulatory component can identify guidelines for defining “nocturnal” (e.g., the start time when a person tries to go to sleep), an example of which can be a measure defined as what % of time patient is aware out of their Total Sleep Opportunity (TSO) time. Additional examples of regulatory guidelines may be standard guidelines (e.g., guidelines promulgated by the Food and Drug Administration (FDA) such as FDA patient-reported outcome (PRO) guidance or FDA's Patient Focused Drug Development (PFDD) guidance series).
In various embodiments, the regulatory component includes guidelines that are helpful for achieving regulatory acceptance. For example, different regulatory pathways involve different requirements to achieve regulatory acceptance. In some scenarios, requests can be submitted through FDA CPIM meetings, within an IND, or through the formal qualification procedure. In particular embodiments, the regulatory component assesses one or more other components of the measurement stack, such as components of the measurement definition asset and/or other components of the evidence asset. Thus, the regulatory component enables the saving of resources by involving the regulators early on as the context of use (COU), digital measures (medical device, digital biomarker, clinical outcome assessment), and all validations (e.g., technical, analytical, and clinical validation) can be approved.
In various embodiments, the regulatory component can be made available to third parties, such as regulators, who can further collaborate on co-developing and/or proposing improvements to standardized solutions. This enables a dynamic regulatory evaluation of standardized solutions. For example, regulators can provide new evidence requests and questions in more real time. In response, new evidence, comments, and additional context can be provided to the regulators. In various embodiments, the dynamic regulatory evaluation involves multiple stakeholders (e.g., involving customers, asset developers, pharmaceutical companies, regulators, etc.) and therefore, the regulatory component can be made available to the multiple stakeholders to enable a collaborative approach towards achieving regulatory approval of the standardized solution. An example of dynamic regulatory evaluation is described below in Example 5.
In various embodiments, regulators may evaluate the standardized solutions (e.g., DMSs) for tolerance and/or bias. Here, the regulatory component can provide guidelines for understanding the size of the expected treatment effect. If the effect is massive, tolerance can be greater, if the effect is minuscule, the measure also needs to be more precise. In various embodiments, the regulatory component can involve regulatory advice that is given independent of the intervention (e.g., which measures are meaningful).
Generally, TSPs are considered solution-agnostic (e.g., no specific brands and versions are named). TIPs are instrumentation-centered and agnostic of certain components of the measurement definition asset (e.g., condition, meaningful aspect of health, hypothesis) as well as certain components of the evidence asset (e.g., clinical validation and regulatory intel). In addition, TIPs are considered condition-agnostic as no components of the TIP layers are associated with a specific condition, meaningful aspect of health, or patient population. This adds new value to the available assets provided by stakeholders and fitting these TIPs. For example, TIPs can be interchangeable across different TSPs that are designed for specific conditions. Therefore, developers (e.g., developers of individual components or assets) can develop functional assets covering multiple conditions. This is in contrast to having developers develop new assets for every specific condition and study design. Novel developments of individual assets may be a waste of resources as often the desired assets might already be available as off-the-shelf solutions. Furthermore, assets included in TIPs can readily be repurposed for multiple conditions. By implementing clinical validation for a specific condition, TIPs are applicable across various conditions, thereby allowing for improved reusability and sustainability of available assets. For example, a class of actigraphy solutions is validated to measure daily life physical activity (DLPA) for a first condition of pulmonary arterial hypertension. Analytical validation has validated the ability to measure DLPA parameters by devices included in this TIP. Additionally, DLPA can also be a concept of interest in a second condition of Parkinson's Disease (PD). Therefore, the validated TIP for measuring DLPA can be repurposed for the second condition of PD without the need to re-perform the technical and analytical validation steps, as these are already included in the TIP. Additionally, TIPs provide a structure to available assets, preventing stakeholders from being overwhelmed by the countless TSPs, devices, and algorithms accessible for digital measures. As a result, solutions within one TIP can be easily compared to deliver the best fit-for-purpose solution for the novel study design. This improves the likelihood that the best instrumentation is picked for specific use cases.
As shown in
As described herein, a TSP encompasses a full measurement stack. The generation and maintenance of TSPs can be performed by the TSP module 145, as described above in
TSPs provide a generic description that covers multiple DMSs within the same solution class. The DMSs that fit in the class represented by the TSP can be considered fit-for-purpose for the same use case. As a result of TSPs being device technology agnostic, included DMSs show improved reusability of assets. Instead of being solely purposed for one study, TSPs accelerate the repurposing of available assets and increase the value of all assets included in DMSs. Also, TSP-classes can be leveraged to compare slight differences between similar TSPs. This allows stakeholders to compare multiple TSPs (and DMSs in the class represented by a TSP) with more ease to select the best preferences for their specific use-case (e.g., costs, the weight of the device, or battery life). In various embodiments, a DMS can also fit the generic description of various TSPs.
Furthermore, TSPs allow for versioning (life cycle management of digital measurement solutions). In time, available devices, algorithms, and technologies evolve. In various embodiments, the TSPs can be edited and modified, resulting in updated versions (which can co-exist with older versions). This allows for components of the solution to be upgraded if the solution overall still meets the TSP criteria. The validation of evolving TSPs is assessed by qualification protocols (QPs). QPs validate versioning and ensure TSPs are considered future-proof In various embodiments, QPs allow for the versioning of specific assets and the ability to validate comparability between multiple DMSs. Qualification protocols are further described in.
Referring to the measurement method component, it describes a device agnostic measurement method. For example, the measurement method component can specify a particular class of devices. Examples of a class devices can include, but are not limited to: any of wearable devices, ingestibles, image and voice based devices, touchless sensors, and sensor based smart devices (e.g., scales, thermometers, and respirators). The measurement method component can further include specifications of the measurement method e.g., battery life, data storage, available measure frequencies). Thus, a developer can determine whether the TSP is appropriate for their digital solution based on the specifications of the measurement method (e.g., if the developer needs to capture data for at least 96 hours, but the TSP measurement method specifies a battery life of 18 hours, then the developer determines that a different TSP is needed).
Referring to the raw data component of the TSP, it describes the raw file that is captured according to the measurement method. For example, the raw data according to the specifications of the measurement method. Therefore, if the measurement method indicates a measurement frequency of 100 Hz, the raw data component describes a raw file that includes data captured at the 100 Hz measurement frequency. In various embodiments, digital measurements reported by measurement methods are derived through a data supply chain, which includes hardware, firmware, and software components. The term “raw data” is used to describe data existing in an early stage of the data supply chain. Sensor output data at the sample level (for example, a 50 Hz accelerometer signal or a 250 Hz ECG signal) would be raw data. Although signal processing methods may have been applied to this data (e.g., down sampling, filtering, interpolation, smoothing, etc.), the data are still considered “raw” because it is a direct representation of the original signal produced by the sensor.
Referring to the algorithm component of the TSP, it identifies one or more algorithms that can appropriately transform the raw data into meaningful health data. Here, the algorithm is designed according to the specific measurement method that was used to capture the raw data. As an example, if the measurement method indicates a measurement frequency of 100 Hz, then the algorithm is designed to transform the data that was specifically captured at a frequency of Hz. In various embodiments, the algorithm component represents a range of data manipulation processes embedded in firmware and software, including but not limited to signal processing, data compression and decompression, artificial intelligence, and machine learning. An algorithm is a calculation that transforms the data from the sensor into meaningful information. The algorithms may be part of the sensor directly, or may be operated by a party to conduct additional data science to create a derived measure.
Referring to the analytical validation component, it enables the validation of the components of the instrumentation asset (e.g., measurement method, raw data, algorithm, and health data) to ensure that the raw data and/or health data is reliable, valid, and sensitive to meet appropriate standards. In various embodiments, the analytical validation occurs at the intersection of engineering and clinical expertise. It involves evaluation of the processed data and requires testing with human subjects. After verified sample-level data have been generated by a measurement method, algorithms are applied to these data in order to create behaviorally or physiologically meaningful metrics. This process begins at the point at which verified output data (sample-level data), becomes the data input for algorithmic processing. Therefore, the first step of analytical validation requires a defined data capture protocol and a specified test subject population. During the process of analytical validation, the metric produced by the algorithm is evaluated against an appropriate reference standard.
In various embodiments, a TSP can be built using a condition-focused approach (e.g., bottom-up approach). In various embodiments, a TSP can be built using an instrumentation-focused approach (e.g., top-down approach). Regardless of the approach (e.g., bottom up or top down), the final TSP and DMS(s) of the class can be identical.
Referring first to the condition-focused approach, a specific condition is identified. Here, a measurement definition meaningful for patients with the condition is determined. This includes determining the concept of interest that will be measured. Next, suitable instrumentation is developed, or, if available, off-the-shelf solutions could be selected. For example, an instrumentation asset of a different TSP could be selected and repurposed for this current TSP. Given that the instrumentation asset of TSPs is generally described in generic terms, the repurposing of the instrumentation asset for the current TSP can require little or no additional work. Next, an evidence asset is generated for the TSP. In various embodiments, generating an evidence asset involves determining technical and analytical validations that are appropriate for the instrumentation of the TSP. In various embodiments, components of the evidence asset, such as components for performing technical and analytical validations, can be repurposed from another TSP. Given that technical and analytical validations may have previously been performed for a generic instrumentation asset of another TSP, the current TSP need not re-perform the same technical and analytical validations again. In various embodiments, a component of the evidence asset includes a clinical validation component. Here, the clinical validation component verifies whether results are clinically valid. Thus, generating an evidence asset includes generating the clinical validation component that is valuable for ensuring the results of the TSP are of clinical value.
Referring to the instrumentation-focused approach, it begins with the discovery of available components. Components with similar instrumentation are identified and thereafter profiled into classes of solutions. These individual components are profiled into individual component classes known as TCPs (solely one generically described layer of the measurement stack, e.g., measurement method or algorithm). Multiple TCPs of different layers can be united into TIPs if completed with the concept of interest, measurement method, raw data, algorithm, health data, and technical verification/analytical validation. TSPs are built on top of these TIPs with aligned definitions and validated instrumentation by completing them with the condition-related layers. For example, TSPs are built by generating the meaningful aspect of health component and clinical validation component on top of the components of the TIPs.
The instrumentation-focused approach eases the development of TCPs, multiple TIPs, and numerous TSPs. In the instrumentation-focused approach, smaller stakeholders could easier contribute to the development of assets, as they often have assets as measurement methods and algorithms within their digital portfolios. Asset providers can now focus more on the development of one component, which can be useful for multiple studies.
In various embodiments, given a full TSP, assets and components of the TSP can be quickly drafted. For example, TIPs and TCPs can quickly be drafted from a complete TSP by excluding the definition-related layers or by picking one individual layer, respectively.
As described herein, assets and components of TSPs can be interchangeable and substituted. For example, a TIP from a first TSP can be substituted for in place of a second TIP in a second TSP. Here, substituting a TIP can be beneficial as it minimizes the validations that are required in view of the substitution. For example, only a technical validation/analytical validation for TSP including the now substituted TSP is re-assessed. As another example, only a clinical validation for the TSP including the now substituted TSP is re-assessed (e.g., technical/analytical validation need not be performed).
Referring first to the DMS shown in
The algorithm component of DMS #1 identifies a specific algorithm (e.g., an ActiGraph deterministic algorithm such as ActiLife 6) that can transform raw data captured by the Actigraph GT9X Link device into meaningful health datasets. Again, in contrast to the corresponding TSP which generically described algorithms, the algorithm component of DMS #1 identifies a specific algorithm that can be executed.
Referring next to the DMS shown in
Here, the DMSs (e.g., DMS #1 and DMS #2) shown in
In various embodiments, a component of DMS #1 and DMS #2 can be interchangeable. For example, the measurement method component of DMS #1 can replace the measurement method component of DMS #2. As another example, the algorithm component of DMS #1 can replace the algorithm component of DMS #2. In various embodiments, a set of components of DMS #1 and DMS #2 can be interchangeable. For example, the measurement method component and algorithm component of DMS #1 can replace the measurement method component and algorithm component of DMS #2, respectively. As another example, the instrumentation asset of DMS #1 (e.g., measurement method component, raw data component, algorithm component, and health data component) can replace the corresponding instrumentation asset of DMS #2. As another example, the target instrumentation profile of DMS #1 (e.g., concept of interest, measurement method component, raw data component, algorithm component, health data component, and analytical validation component) can replace the corresponding target instrumentation profile of DMS #2.
Further examples of digital measurement solutions (DMSs) are further detailed in Table 5.
Digital measurement solutions are subject to a rapidly evolving lifecycle as the components of the instrumentation asset are always facing the possibility of being upgraded (e.g., due to new device release or new software release). Managing the rapid technological evolution while maintaining equivalency between different measurement solutions and versions is a new challenge. These upgrades, for example, can be bug fixes or add novel features to available devices.
In various embodiments, qualification protocols are implemented to improve the life cycle management of rapidly evolving components in relation to their TSPs. The implementation of qualification protocols can be performed by the qualification protocol module 155 (see
In various embodiments, a QP evaluates a new TSP and/or new DMS in view of an upgraded device or software release, and upon a successful validation, the new TSP or DMS can be stored e.g., as part of the marketplace or catalog. Here, the new TSP or DMS can replace the prior solution (e.g., prior TSP or prior DMS). In various embodiments, this can involve replacing hardware components and/or delivering software upgrades. In various embodiments, the new TSP or DMS that has been validated using a QP can represent an additional asset e.g., for inclusion in the marketplace or catalog. For example, the new DMS can include the upgraded device or upgraded software and therefore, can be used to characterize a disease. This can be in addition to the prior DMS that includes a prior version of the device or software, which continues to be a solution for characterizing the disease, albeit with older hardware/software.
QPs can involve an evidence-based validation process that enables improved life cycle management of TSPs. These QPs represent standardized experiments that generate evidence that the overall solution from a TSP performs at a sufficient level for its intended purpose. In various embodiments, QPs are fully automated for validating, for example, upgraded algorithms. Thus, results of the upgraded algorithms can be referenced against available reference datasets. In various embodiments, QPs involve controlled experiments. For example, if a new, upgraded device is released, a QP can involve evaluating the results of the upgraded device in comparison to a prior version of the device by using both devices on a patient population.
In particular embodiments, QPs are implemented to ensure that the upgraded TSP or DMS (e.g., due to incorporation of upgraded device or software) achieves comparable results in comparison to prior versions of the TSP or DMS. In various embodiments, raw data captured by a device of an older version of a DMS and a raw data captured by a new device of a newer version of a DMS are comparable if the difference between the raw data are less than a threshold number. In various embodiments, the threshold number is 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20%. In particular embodiments, the threshold number is 10%. In particular embodiments, the threshold number is 5%. In particular embodiments, the threshold number is 2%. In various embodiments, meaningful health data transformed from raw data captured by a device of an older version of a DMS and meaningful health data transformed from raw data captured by a new device of a newer version of a DMS are comparable if the difference between the different meaningful health data are less than a threshold number. In various embodiments, the threshold number is 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20%. In particular embodiments, the threshold number is 10%. In particular embodiments, the threshold number is 5%. In particular embodiments, the threshold number is 2%.
In various embodiments, upon a successful validation using a QP, the upgraded TSP or upgraded DMS can be annotated accordingly. For example, the metadata of the TSP or DMS can be annotated with an indication that a validation using a QP was successfully performed. In various embodiments, the metadata may be available for inspection by third parties (e.g., through a catalog or marketplace). Thus, a third party can readily be informed of TSPs and/or DMSs that have been successfully validated using a QP.
To provide an example, an actigraphy-assessed TSP with a daily life physical activity-endpoint (COI=gait speed), covers numerous smartwatch devices (e.g., the Apple Watch 5, GENEActiv Original watch, ActiGraph GT9X Link). Over time, a new smartwatch device, such as the Apple Watch 6, is released. The QP is implemented to ensure that the outcomes measured by DMSs due to the upgraded device remain valid. An example how this could be assessed using a qualification protocol is as follows:
1. Participants of a small group (N=20) wear both the old and the new device (e.g., on either wrist—if wrist-worn devices). Participants can be healthy individuals or, alternatively, can include patient populations. For example, if there is no difference in measuring gait speed between healthy participants and patient populations, the patient participants can be included as participants.
2. With a concept of interest being gait speed, participants are asked to perform physical activity related tasks as walking, running and walking the stairs while wearing both the old and new device.
3. Raw data is continuously captured for a prolonged period of time (e.g., 5 days) and corresponding algorithms (same or new) translate the raw dataset into meaningful health data sets with gait speed evidence.
4. If the translated health datasets are within a comparable range (e.g., <2%), both devices are validated as comparable and the Apple Watch 6 can now be considered for the same research purposes as its older device. Conversely, if the QP cannot validate the new device as generating comparable results, the manufacturer can decide to re-assess the new device to ensure that it will be validated by the QP in a second assessment.
In various embodiments, a new device release or new software release can exceed the specifications of a TSP. For example, assume that the newly released Apple Watch 6 acquires raw data at a frequency range between 32-256 Hz. This may exceed the specifications of the TSP (e.g., 1-100 Hz frequencies). Thus, a new TSP or upgraded TSP that incorporates the broader device specification (e.g., broader frequencies) can be generated and validated using the QPs.
Step 355 involves obtaining a measurement of interest. Here, the measurement of interest can be captured using a measurement method specified by a digital measurement solution. For example, the measurement of interest can be captured using a particular device having specifications (e.g., data storage, battery life, measurement frequency) that are specified in the digital measurement solution (e.g., in the measurement method component).
Step 360 involves selecting a DMS from a plurality of DMS of a common class represented by a target solution profile. Here, the selected DMS specifies the measurement method by which the measurement of interest was captured in step 355.
Step 365 involves applying one or more components of the DMS to the obtained measurement of interest to characterize the disease for the subject. For example, step 365 can involve applying an algorithm specified in the algorithm component of the DMS. The algorithm transforms raw data of the measurement of interest to a meaningful health dataset. Thus, the disease of the subject can be characterized according to the meaningful health dataset.
Step 385 involves receiving a selection of one or more of the TSPs in the catalog. For example, a third party may select a TSP that suit their needs.
Step 390 involves providing the selected TSP or one or more digital measurement solutions that are of a common class represented by the selected TSP. In various embodiments, the selected TSP is provided. In various embodiments, the one or more digital measurement solutions is provided.
Disclosed herein are TSPs and DMSs that are built and implemented for specific diseases or conditions. In various embodiments, the disease can be, for example, a cancer, inflammatory disease, neurodegenerative disease, neurological disease, autoimmune disorder, neuromuscular disease, metabolic disorder (e.g., diabetes), cardiac disease, or fibrotic disease.
In various embodiments, the cancer can be any one of lung bronchioloalveolar carcinoma (BAC), bladder cancer, a female genital tract malignancy (e.g., uterine serous carcinoma, endometrial carcinoma, vulvar squamous cell carcinoma, and uterine sarcoma), an ovarian surface epithelial carcinoma (e.g., clear cell carcinoma of the ovary, epithelial ovarian cancer, fallopian tube cancer, and primary peritoneal cancer), breast carcinoma, non-small cell lung cancer (NSCLC), a male genital tract malignancy (e.g., testicular cancer), retroperitoneal or peritoneal carcinoma, gastroesophageal adenocarcinoma, esophagogastric junction carcinoma, liver hepatocellular carcinoma, esophageal and esophagogastric junction carcinoma, cervical cancer, cholangiocarcinoma, pancreatic adenocarcinoma, extrahepatic bile duct adenocarcinoma, a small intestinal malignancy, gastric adenocarcinoma, cancer of unknown primary (CUP), colorectal adenocarcinoma, esophageal carcinoma, prostatic adenocarcinoma, kidney cancer, head and neck squamous carcinoma, thymic carcinoma, non-melanoma skin cancer, thyroid carcinoma (e.g., papillary carcinoma), a head and neck cancer, anal carcinoma, non-epithelial ovarian cancer (non-EOC), uveal melanoma, malignant pleural mesothelioma, small cell lung cancer (SCLC), a central nervous system cancer, a neuroendocrine tumor, and a soft tissue tumor. For example, in certain embodiments, the cancer is breast cancer, non-small cell lung cancer, bladder cancer, kidney cancer, colon cancer, and melanoma.
In various embodiments, the inflammatory disease can be any one of acute respiratory distress syndrome (ARDS), acute lung injury (ALI), alcoholic liver disease, allergic inflammation of the skin, lungs, and gastrointestinal tract, allergic rhinitis, ankylosing spondylitis, asthma (allergic and non-allergic), atopic dermatitis (also known as atopic eczema), atherosclerosis, celiac disease, chronic obstructive pulmonary disease (COPD), pulmonary arterial hypertension (PAH), chronic respiratory distress syndrome (CRDS), colitis, dermatitis, diabetes, eczema, endocarditis, fatty liver disease, fibrosis (e.g., idiopathic pulmonary fibrosis, scleroderma, kidney fibrosis, and scarring), food allergies (e.g., allergies to peanuts, eggs, dairy, shellfish, tree nuts, etc.), gastritis, gout, hepatic steatosis, hepatitis, inflammation of body organs including joint inflammation including joints in the knees, limbs or hands, inflammatory bowel disease (IBD) (including Crohn's disease or ulcerative colitis), intestinal hyperplasia, irritable bowel syndrome, juvenile rheumatoid arthritis, liver disease, metabolic syndrome, multiple sclerosis, myasthenia gravis, neurogenic lung edema, nephritis (e.g., glomerular nephritis), non-alcoholic fatty liver disease (NAFLD) (including non-alcoholic steatosis and non-alcoholic steatohepatitis (NASH)), obesity, prostatitis, psoriasis, psoriatic arthritis, rheumatoid arthritis (RA), sarcoidosis sinusitis, splenitis, seasonal allergies, sepsis, systemic lupus erythematosus, uveitis, and UV-induced skin inflammation.
In various embodiments, the neurodegenerative disease can be any one of Alzheimer's disease, Parkinson's disease, traumatic CNS injury, Down Syndrome (DS), glaucoma, amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Huntington's disease. In addition, the neurodegenerative disease can also include Absence of the Septum Pellucidum, Acid Lipase Disease, Acid Maltase Deficiency, Acquired Epileptiform Aphasia, Acute Disseminated Encephalomyelitis, ADHD, Adie's Pupil, Adie's Syndrome, Adrenoleukodystrophy, Agenesis of the Corpus Callosum, Agnosia, Aicardi Syndrome, AIDS, Alexander Disease, Alper's Disease, Alternating Hemiplegia, Anencephaly, Aneurysm, Angelman Syndrome, Angiomatosis, Anoxia, Antiphosphipid Syndrome, Aphasia, Apraxia, Arachnoid Cysts, Arachnoiditis, Arnold-Chiari Malformation, Arteriovenous Malformation, Asperger Syndrome, Ataxia, Ataxia Telangiectasia, Ataxias and Cerebellar or Spinocerebellar Degeneration, Autism, Autonomic Dysfunction, Barth Syndrome, Batten Disease, Becker's Myotonia, Behcet's Disease, Bell's Palsy, Benign Essential Blepharospasm, Benign Focal Amyotrophy, Benign Intracranial Hypertension, Bernhardt-Roth Syndrome, Binswanger's Disease, Blepharospasm, Bloch-Sulzberger Syndrome, Brachial Plexus Injuries, Bradbury-Eggleston Syndrome, Brain or Spinal Tumors, Brain Aneurysm, Brain injury, Brown-Sequard Syndrome, Bulbospinal Muscular Atrophy, Cadasil, Canavan Disease, Causalgia, Cavernomas, Cavernous Angioma, Central Cord Syndrome, Central Pain Syndrome, Central Pontine Myelinolysis, Cephalic Disorders, Ceramidase Deficiency, Cerebellar Degeneration, Cerebellar Hypoplasia, Cerebral Aneurysm, Cerebral Arteriosclerosis, Cerebral Atrophy, Cerebral Beriberi, Cerebral Gigantism, Cerebral Hypoxia, Cerebral Palsy, Cerebro-Oculo-Facio-Skeletal Syndrome, Charcot-Marie-Tooth Disease, Chiari Malformation, Chorea, Chronic Inflammatory Demyelinating Polyneuropathy (CIDP), Coffin Lowry Syndrome, Colpocephaly, Congenital Facial Diplegia, Congenital Myasthenia, Congenital Myopathy, Corticobasal Degeneration, Cranial Arteritis, Craniosynostosis, Creutzfeldt-Jakob Disease, Cumulative Trauma Disorders, Cushing's Syndrome, Cytomegalic Inclusion Body Disease, Dancing Eyes-Dancing Feet Syndrome, Dandy-Walker Syndrome, Dawson Disease, Dementia, Dementia With Lewy Bodies, Dentate Cerebellar Ataxia, Dentatorubral Atrophy, Dermatomyositis, Developmental Dyspraxia, Devic's Syndrome, Diabetic Neuropathy, Diffuse Sclerosis, Dravet Syndrome, Dysautonomia, Dysgraphia, Dyslexia, Dysphagia, Dyssynergia Cerebellaris Myoclonica, Dystonias, Early Infantile Epileptic Encephalopathy, Empty Sella Syndrome, Encephalitis, Encephalitis Lethargica, Encephaloceles, Encephalopathy, Encephalotrigeminal Angiomatosis, Epilepsy, Erb-Duchenne and Dejerine-Klumpke Palsies, Erb's Palsy, Essential Tremor, Extrapontine Myelinolysis, Fabry Disease, Fahr's Syndrome, Fainting, Familial Dysautonomia, Familial Hemangioma, Familial Periodic Paralyzes, Familial Spastic Paralysis, Farber's Disease, Febrile Seizures, Fibromuscular Dysplasia, Fisher Syndrome, Floppy Infant Syndrome, Foot Drop, Friedreich's Ataxia, Frontotemporal Dementia, Gangliosidoses, Gaucher's Disease, Gerstmann's Syndrome, Gerstmann-Straussler-Scheinker Disease, Giant Cell Arteritis, Giant Cell Inclusion Disease, Globoid Cell Leukodystrophy, Glossopharyngeal Neuralgia, Glycogen Storage Disease, Guillain-Barre Syndrome, Hallervorden-Spatz Disease, Head Injury, Hemicrania Continua, Hemifacial Spasm, Hemiplegia Alterans, Hereditary Neuropathy, Hereditary Spastic Paraplegia, Heredopathia Atactica Polyneuritiformis, Herpes Zoster, Herpes Zoster Oticus, Hirayama Syndrome, Holmes-Adie syndrome, Holoprosencephaly, HTLV-1 Associated Myelopathy, Hughes Syndrome, Huntington's Disease, Hydranencephaly, Hydrocephalus, Hydromyelia, Hypernychthemeral Syndrome, Hypersomnia, Hypertonia, Hypotonia, Hypoxia, Immune-Mediated Encephalomyelitis, Inclusion Body Myositis, Incontinentia Pigmenti, Infantile Hypotonia, Infantile Neuroaxonal Dystrophy, Infantile Phytanic Acid Storage Disease, Infantile Refsum Disease, Infantile Spasms, Inflammatory Myopathies, Iniencephaly, Intestinal Lipodystrophy, Intracranial Cysts, Intracranial Hypertension, Isaac's Syndrome, Joubert syndrome, Kearns-Sayre Syndrome, Kennedy's Disease, Kinsbourne syndrome, Kleine-Levin Syndrome, Klippel-Feil Syndrome, Klippel-Trenaunay Syndrome (KTS), Kluver-Bucy Syndrome, Korsakoff s Amnesic Syndrome, Krabbe Disease, Kugelberg-Welander Disease, Kuru, Lambert-Eaton Myasthenic Syndrome, Landau-Kleffner Syndrome, Lateral Medullary Syndrome, Learning Disabilities, Leigh's Disease, Lennox-Gastaut Syndrome, Lesch-Nyhan Syndrome, Leukodystrophy, Levine-Critchley Syndrome, Lewy Body Dementia, Lipid Storage Diseases, Lipoid Proteinosis, Lissencephaly, Locked-In Syndrome, Lou Gehrig's Disease, Lupus, Lyme Disease, Machado-Joseph Disease, Macrencephaly, Melkersson-Rosenthal Syndrome, Meningitis, Menkes Disease, Meralgia Paresthetica, Metachromatic Leukodystrophy, Microcephaly, Migraine, Miller Fisher Syndrome, Mini-Strokes, Mitochondrial Myopathies, Motor Neuron Diseases, Moyamoya Disease, Mucolipidoses, Mucopolysaccharidoses, Multiple sclerosis (MS), Multiple System Atrophy, Muscular Dystrophy, Myasthenia Gravis, Myoclonus, Myopathy, Myotonia, Narcolepsy, Neuroacanthocytosis, Neurodegeneration with Brain Iron Accumulation, Neurofibromatosis, Neuroleptic Malignant Syndrome, Neurosarcoidosis, Neurotoxicity, Nevus Cavernosus, Niemann-Pick Disease, Non 24 Sleep Wake Disorder, Normal Pressure Hydrocephalus, Occipital Neuralgia, Occult Spinal Dysraphism Sequence, Ohtahara Syndrome, Olivopontocerebellar Atrophy, Opsoclonus Myoclonus, Orthostatic Hypotension, O'Sullivan-McLeod Syndrome, Overuse Syndrome, Pantothenate Kinase-Associated Neurodegeneration, Paraneoplastic Syndromes, Paresthesia, Parkinson's Disease, Paroxysmal Choreoathetosis, Paroxysmal Hemicrania, Parry-Romberg, Pelizaeus-Merzbacher Disease, Perineural Cysts, Periodic Paralyzes, Peripheral Neuropathy, Periventricular Leukomalacia, Pervasive Developmental Disorders, Pinched Nerve, Piriformis Syndrome, Plexopathy, Polymyositis, Pompe Disease, Porencephaly, Postherpetic Neuralgia, Postinfectious Encephalomyelitis, Post-Polio Syndrome, Postural Hypotension, Postural Orthostatic Tachyardia Syndrome (POTS), Primary Lateral Sclerosis, Prion Diseases, Progressive Multifocal Leukoencephalopathy, Progressive Sclerosing Poliodystrophy, Progressive Supranuclear Palsy, Prosopagnosia, Pseudotumor Cerebri, Ramsay Hunt Syndrome I, Ramsay Hunt Syndrome II, Rasmussen's Encephalitis, Reflex Sympathetic Dystrophy Syndrome, Refsum Disease, Refsum Disease, Repetitive Motion Disorders, Repetitive Stress Injuries, Restless Legs Syndrome, Retrovirus-Associated Myelopathy, Rett Syndrome, Reye's Syndrome, Rheumatic Encephalitis, Riley-Day Syndrome, Saint Vitus Dance, Sandhoff Disease, Schizencephaly, Septo-Optic Dysplasia, Shingles, Shy-Drager Syndrome, Sjogren's Syndrome, Sleep Apnea, Sleeping Sickness, Sotos Syndrome, Spasticity, Spinal Cord Infarction, Spinal Cord Injury, Spinal Cord Tumors, Spinocerebellar Atrophy, Spinocerebellar Degeneration, Stiff-Person Syndrome, Striatonigral Degeneration, Stroke, Sturge-Weber Syndrome, SUNCT Headache, Syncope, Syphilitic Spinal Sclerosis, Syringomyelia, Tabes Dorsalis, Tardive Dyskinesia, Tarlov Cysts, Tay-Sachs Disease, Temporal Arteritis, Tethered Spinal Cord Syndrome, Thomsen's Myotonia, Thoracic Outlet Syndrome, Thyrotoxic Myopathy, Tinnitus, Todd's Paralysis, Tourette Syndrome, Transient Ischemic Attack, Transmissible Spongiform Encephalopathies, Transverse Myelitis, Traumatic Brain Injury, Tremor, Trigeminal Neuralgia, Tropical Spastic Paraparesis, Troyer Syndrome, Tuberous Sclerosis, Vasculitis including Temporal Arteritis, Von Economo's Disease, Von Hippel-Lindau Disease (VHL), Von Recklinghausen's Disease, Wallenberg's Syndrome, Werdnig-Hoffman Disease, Wernicke-Korsakoff Syndrome, West Syndrome, Whiplash, Whipple's Disease, Williams Syndrome, Wilson's Disease, Wolman's Disease, X-Linked Spinal and Bulbar Muscular Atrophy, and Zellweger Syndrome.
In various embodiments, the autoimmune disease or disorder can be any one of: arthritis, including rheumatoid arthritis, acute arthritis, chronic rheumatoid arthritis, gout or gouty arthritis, acute gouty arthritis, acute immunological arthritis, chronic inflammatory arthritis, degenerative arthritis, type II collagen-induced arthritis, infectious arthritis, Lyme arthritis, proliferative arthritis, psoriatic arthritis, Still's disease, vertebral arthritis, juvenile-onset rheumatoid arthritis, osteoarthritis, arthritis deformans, polyarthritis chronica primaria, reactive arthritis, and ankylosing spondylitis; inflammatory hyperproliferative skin diseases; psoriasis, such as plaque psoriasis, pustular psoriasis, and psoriasis of the nails; atopy, including atopic diseases such as hay fever and Job's syndrome; dermatitis, including contact dermatitis, chronic contact dermatitis, exfoliative dermatitis, allergic dermatitis, allergic contact dermatitis, dermatitis herpetiformis, nummular dermatitis, seborrheic dermatitis, non-specific dermatitis, primary irritant contact dermatitis, and atopic dermatitis; x-linked hyper IgM syndrome; allergic intraocular inflammatory diseases; urticaria, such as chronic allergic urticaria, chronic idiopathic urticaria, and chronic autoimmune urticaria; myositis; polymyositis/dermatomyositis; juvenile dermatomyositis; toxic epidermal necrolysis; scleroderma, including systemic scleroderma; sclerosis, such as systemic sclerosis, multiple sclerosis (MS), spino-optical MS, primary progressive MS (PPMS), relapsing remitting MS (RRMS), progressive systemic sclerosis, atherosclerosis, arteriosclerosis, sclerosis disseminata, and ataxic sclerosis; neuromyelitis optica (NMO); inflammatory bowel disease (IBD), including Crohn's disease, autoimmune-mediated gastrointestinal diseases, colitis, ulcerative colitis, colitis ulcerosa, microscopic colitis, collagenous colitis, colitis polyposa, necrotizing enterocolitis, transmural colitis, and autoimmune inflammatory bowel disease; bowel inflammation; pyoderma gangrenosum; erythema nodosum; primary sclerosing cholangitis; respiratory distress syndrome, including adult or acute respiratory distress syndrome (ARDS); meningitis; inflammation of all or part of the uvea; iritis; choroiditis; an autoimmune hematological disorder; rheumatoid spondylitis; rheumatoid synovitis; hereditary angioedema; cranial nerve damage, as in meningitis; herpes gestationis; pemphigoid gestationis; pruritis scroti; autoimmune premature ovarian failure; sudden hearing loss due to an autoimmune condition; IgE-mediated diseases, such as anaphylaxis and allergic and atopic rhinitis; encephalitis, such as Rasmussen's encephalitis and limbic and/or brainstem encephalitis; uveitis, such as anterior uveitis, acute anterior uveitis, granulomatous uveitis, nongranulomatous uveitis, phacoantigenic uveitis, posterior uveitis, or autoimmune uveitis; glomerulonephritis (GN) with and without nephrotic syndrome, such as chronic or acute glomerulonephritis, primary GN, immune-mediated GN, membranous GN (membranous nephropathy), idiopathic membranous GN or idiopathic membranous nephropathy, membrano- or membranous proliferative GN (MPGN), including Type I and Type II, and rapidly progressive GN; proliferative nephritis; autoimmune polyglandular endocrine failure; balanitis, including balanitis circumscripta plasmacellularis; balanoposthitis; erythema annulare centrifugum; erythema dyschromicum perstans; eythema multiform; granuloma annulare; lichen nitidus; lichen sclerosus et atrophicus; lichen simplex chronicus; lichen spinulosus; lichen planus; lamellar ichthyosis; epidermolytic hyperkeratosis; premalignant keratosis; pyoderma gangrenosum; allergic conditions and responses; allergic reaction; eczema, including allergic or atopic eczema, asteatotic eczema, dyshidrotic eczema, and vesicular palmoplantar eczema; asthma, such as asthma bronchiale, bronchial asthma, and auto-immune asthma; conditions involving infiltration of T cells and chronic inflammatory responses; immune reactions against foreign antigens such as fetal A-B-O blood groups during pregnancy; chronic pulmonary inflammatory disease; autoimmune myocarditis; leukocyte adhesion deficiency; lupus, including lupus nephritis, lupus cerebritis, pediatric lupus, non-renal lupus, extra-renal lupus, discoid lupus and discoid lupus erythematosus, alopecia lupus, systemic lupus erythematosus (SLE), cutaneous SLE, subacute cutaneous SLE, neonatal lupus syndrome (NLE), and lupus erythematosus disseminatus; juvenile onset (Type I) diabetes mellitus, including pediatric insulin-dependent diabetes mellitus (IDDM), adult onset diabetes mellitus (Type II diabetes), autoimmune diabetes, idiopathic diabetes insipidus, diabetic retinopathy, diabetic nephropathy, and diabetic large-artery disorder; immune responses associated with acute and delayed hypersensitivity mediated by cytokines and T-lymphocytes; tuberculosis; sarcoidosis; granulomatosis, including lymphomatoid granulomatosis; Wegener's granulomatosis; agranulocytosis; vasculitides, including vasculitis, large-vessel vasculitis, polymyalgia rheumatica and giant-cell (Takayasu's) arteritis, medium-vessel vasculitis, Kawasaki's disease, polyarteritis nodosa/periarteritis nodosa, microscopic polyarteritis, immunovasculitis, CNS vasculitis, cutaneous vasculitis, hypersensitivity vasculitis, necrotizing vasculitis, systemic necrotizing vasculitis, ANCA-associated vasculitis, Churg-Strauss vasculitis or syndrome (CSS), and ANCA-associated small-vessel vasculitis; temporal arteritis; aplastic anemia; autoimmune aplastic anemia; Coombs positive anemia; Diamond Blackfan anemia; hemolytic anemia or immune hemolytic anemia, including autoimmune hemolytic anemia (AIHA), pernicious anemia (anemia perniciosa); Addison's disease; pure red cell anemia or aplasia (PRCA); Factor VIII deficiency; hemophilia A; autoimmune neutropenia; pancytopenia; leukopenia; diseases involving leukocyte diapedesis; CNS inflammatory disorders; multiple organ injury syndrome, such as those secondary to septicemia, trauma or hemorrhage; antigen-antibody complex-mediated diseases; anti-glomerular basement membrane disease; anti-phospholipid antibody syndrome; allergic neuritis; Behcet's disease/syndrome; Castleman's syndrome; Goodpasture's syndrome; Reynaud's syndrome; Sjogren's syndrome; Stevens-Johnson syndrome; pemphigoid, such as pemphigoid bullous and skin pemphigoid, pemphigus, pemphigus vulgaris, pemphigus foliaceus, pemphigus mucus-membrane pemphigoid, and pemphigus erythematosus; autoimmune polyendocrinopathies; Reiter's disease or syndrome; thermal injury; preeclampsia; an immune complex disorder, such as immune complex nephritis, and antibody-mediated nephritis; polyneuropathies; chronic neuropathy, such as IgM polyneuropathies and IgM-mediated neuropathy; thrombocytopenia (as developed by myocardial infarction patients, for example), including thrombotic thrombocytopenic purpura (TTP), post-transfusion purpura (PTP), heparin-induced thrombocytopenia, autoimmune or immune-mediated thrombocytopenia, idiopathic thrombocytopenic purpura (ITP), and chronic or acute ITP; scleritis, such as idiopathic cerato-scleritis, and episcleritis; autoimmune disease of the testis and ovary including, autoimmune orchitis and oophoritis; primary hypothyroidism; hypoparathyroidism; autoimmune endocrine diseases, including thyroiditis, autoimmune thyroiditis, Hashimoto's disease, chronic thyroiditis (Hashimoto's thyroiditis), or subacute thyroiditis, autoimmune thyroid disease, idiopathic hypothyroidism, Grave's disease, polyglandular syndromes, autoimmune polyglandular syndromes, and polyglandular endocrinopathy syndromes; paraneoplastic syndromes, including neurologic paraneoplastic syndromes; Lambert-Eaton myasthenic syndrome or Eaton-Lambert syndrome; stiff-man or stiff-person syndrome; encephalomyelitis, such as allergic encephalomyelitis, encephalomyelitis allergica, and experimental allergic encephalomyelitis (EAE); myasthenia gravis, such as thymoma-associated myasthenia gravis; cerebellar degeneration; neuromyotonia; opsoclonus or opsoclonus myoclonus syndrome (OMS); sensory neuropathy; multifocal motor neuropathy; Sheehan's syndrome; hepatitis, including autoimmune hepatitis, chronic hepatitis, lupoid hepatitis, giant-cell hepatitis, chronic active hepatitis, and autoimmune chronic active hepatitis; lymphoid interstitial pneumonitis (LIP); bronchiolitis obliterans (non-transplant) vs NSIP; Guillain-Barre syndrome; Berger's disease (IgA nephropathy); idiopathic IgA nephropathy; linear IgA dermatosis; acute febrile neutrophilic dermatosis; subcorneal pustular dermatosis; transient acantholytic dermatosis; cirrhosis, such as primary biliary cirrhosis and pneumonocirrhosis; autoimmune enteropathy syndrome; Celiac or Coeliac disease; celiac sprue (gluten enteropathy); refractory sprue; idiopathic sprue; cryoglobulinemia; amylotrophic lateral sclerosis (ALS; Lou Gehrig's disease); coronary artery disease; autoimmune ear disease, such as autoimmune inner ear disease (AIED); autoimmune hearing loss; polychondritis, such as refractory or relapsed or relapsing polychondritis; pulmonary alveolar proteinosis; Cogan's syndrome/nonsyphilitic interstitial keratitis; Bell's palsy; Sweet's disease/syndrome; rosacea autoimmune; zoster-associated pain; amyloidosis; a non-cancerous lymphocytosis; a primary lymphocytosis, including monoclonal B cell lymphocytosis (e.g., benign monoclonal gammopathy and monoclonal gammopathy of undetermined significance, MGUS); peripheral neuropathy; channelopathies, such as epilepsy, migraine, arrhythmia, muscular disorders, deafness, blindness, periodic paralysis, and channelopathies of the CNS; autism; inflammatory myopathy; focal or segmental or focal segmental glomerulosclerosis (FSGS); endocrine opthalmopathy; uveoretinitis; chorioretinitis; autoimmune hepatological disorder; fibromyalgia; multiple endocrine failure; Schmidt's syndrome; adrenalitis; gastric atrophy; presenile dementia; demyelinating diseases, such as autoimmune demyelinating diseases and chronic inflammatory demyelinating polyneuropathy; Dressler's syndrome; alopecia areata; alopecia totalis; CREST syndrome (calcinosis, Raynaud's phenomenon, esophageal dysmotility, sclerodactyly, and telangiectasia); male and female autoimmune infertility (e.g., due to anti-spermatozoan antibodies); mixed connective tissue disease; Chagas' disease; rheumatic fever; recurrent abortion; farmer's lung; erythema multiforme; post-cardiotomy syndrome; Cushing's syndrome; bird-fancier's lung; allergic granulomatous angiitis; benign lymphocytic angiitis; Alport's syndrome; alveolitis, such as allergic alveolitis and fibrosing alveolitis; interstitial lung disease; transfusion reaction; leprosy; malaria; Samter's syndrome; Caplan's syndrome; endocarditis; endomyocardial fibrosis; diffuse interstitial pulmonary fibrosis; interstitial lung fibrosis; pulmonary fibrosis; idiopathic pulmonary fibrosis; cystic fibrosis; endophthalmitis; erythema elevatum et diutinum; erythroblastosis fetalis; eosinophilic fasciitis; Shulman's syndrome; Felty's syndrome; flariasis; cyclitis, such as chronic cyclitis, heterochronic cyclitis, iridocyclitis (acute or chronic), or Fuch's cyclitis; Henoch-Schonlein purpura; sepsis; endotoxemia; pancreatitis; thyroxicosis; Evan's syndrome; autoimmune gonadal failure; Sydenham's chorea; post-streptococcal nephritis; thromboangitis ubiterans; thyrotoxicosis; tabes dorsalis; choroiditis; giant-cell polymyalgia; chronic hypersensitivity pneumonitis; keratoconjunctivitis sicca; epidemic keratoconjunctivitis; idiopathic nephritic syndrome; minimal change nephropathy; benign familial and ischemia-reperfusion injury; transplant organ reperfusion; retinal autoimmunity; joint inflammation; bronchitis; chronic obstructive airway/pulmonary disease; silicosis; aphthae; aphthous stomatitis; arteriosclerotic disorders; aspermiogenese; autoimmune hemolysis; Boeck's disease; cryoglobulinemia; Dupuytren's contracture; endophthalmia phacoanaphylactica; enteritis allergica; erythema nodo sum leprosum; idiopathic facial paralysis; febris rheumatica; Hamman-Rich's disease; sensoneural hearing loss; haemoglobinuria paroxysmatica; hypogonadism; ileitis regionalis; leucopenia; mononucleosis infectiosa; traverse myelitis; primary idiopathic myxedema; nephrosis; ophthalmia symphatica; orchitis granulomatosa; pancreatitis; polyradiculitis acuta; pyoderma gangrenosum; Quervain's thyreoiditis; acquired splenic atrophy; non-malignant thymoma; vitiligo; toxic-shock syndrome; food poisoning; conditions involving infiltration of T cells; leukocyte-adhesion deficiency; immune responses associated with acute and delayed hypersensitivity mediated by cytokines and T-lymphocytes; diseases involving leukocyte diapedesis; multiple organ injury syndrome; antigen-antibody complex-mediated diseases; antiglomerular basement membrane disease; allergic neuritis; autoimmune polyendocrinopathies; oophoritis; primary myxedema; autoimmune atrophic gastritis; sympathetic ophthalmia; rheumatic diseases; mixed connective tissue disease; nephrotic syndrome; insulitis; polyendocrine failure; autoimmune polyglandular syndrome type I; adult-onset idiopathic hypoparathyroidism (AOIH); cardiomyopathy such as dilated cardiomyopathy; epidermolisis bullosa acquisita (EBA); hemochromatosis; myocarditis; nephrotic syndrome; primary sclerosing cholangitis; purulent or nonpurulent sinusitis; acute or chronic sinusitis; ethmoid, frontal, maxillary, or sphenoid sinusitis; an eosinophil-related disorder such as eosinophilia, pulmonary infiltration eosinophilia, eosinophilia-myalgia syndrome, Loffler's syndrome, chronic eosinophilic pneumonia, tropical pulmonary eosinophilia, bronchopneumonic aspergillosis, aspergilloma, or granulomas containing eosinophils; anaphylaxis; seronegative spondyloarthritides; polyendocrine autoimmune disease; sclerosing cholangitis; chronic mucocutaneous candidiasis; Bruton's syndrome; transient hypogammaglobulinemia of infancy; Wiskott-Aldrich syndrome; ataxia telangiectasia syndrome; angiectasis; autoimmune disorders associated with collagen disease, rheumatism, neurological disease, lymphadenitis, reduction in blood pressure response, vascular dysfunction, tissue injury, cardiovascular ischemia, hyperalgesia, renal ischemia, cerebral ischemia, and disease accompanying vascularization; allergic hypersensitivity disorders; glomerulonephritides; reperfusion injury; ischemic reperfusion disorder; reperfusion injury of myocardial or other tissues; lymphomatous tracheobronchitis; inflammatory dermatoses; dermatoses with acute inflammatory components; multiple organ failure; bullous diseases; renal cortical necrosis; acute purulent meningitis or other central nervous system inflammatory disorders; ocular and orbital inflammatory disorders; granulocyte transfusion-associated syndromes; cytokine-induced toxicity; narcolepsy; acute serious inflammation; chronic intractable inflammation; pyelitis; endarterial hyperplasia; peptic ulcer; valvulitis; and endometriosis. In particular embodiments, the autoimmune disorder in the subject can include one or more of: systemic lupus erythematosus (SLE), lupus nephritis, chronic graft versus host disease (cGVHD), rheumatoid arthritis (RA), Sjogren's syndrome, vitiligo, inflammatory bowed disease, and Crohn's Disease. In particular embodiments, the autoimmune disorder is systemic lupus erythematosus (SLE). In particular embodiments, the autoimmune disorder is rheumatoid arthritis.
Exemplary metabolic disorders include, for example, diabetes, insulin resistance, lysosomal storage disorders (e.g., Gauchers disease, Krabbe disease, Niemann Pick disease types A and B, multiple sclerosis, Fabry's disease, Tay Sachs disease, and Sandhoff Variant A, B), obesity, cardiovascular disease, and dyslipidemia. Other exemplary metabolic disorders include, for example, 17-alpha-hydroxylase deficiency, 17-beta hydroxysteroid dehydrogenase 3 deficiency, 18 hydroxylase deficiency, 2-hydroxyglutaric aciduria, 2-methylbutyryl-CoA dehydrogenase deficiency, 3-alpha hydroxyacyl-CoA dehydrogenase deficiency, 3-hydroxyisobutyric aciduria, 3-methylcrotonyl-CoA carboxylase deficiency, 3-methylglutaconyl-CoA hydratase deficiency (AUH defect), 5-oxoprolinase deficiency, 6-pyruvoyl-tetrahydropterin synthase deficiency, abdominal obesity metabolic syndrome, abetalipoproteinemia, acatalasemia, aceruloplasminemia, acetyl CoA acetyltransferase 2 deficiency, acetyl-carnitine deficiency, acrodermatitis enteropathica, adenine phosphoribosyltransferase deficiency, adenosine deaminase deficiency, adenosine monophosphate deaminase 1 deficiency, adenylosuccinase deficiency, adrenomyeloneuropathy, adult polyglucosan body disease, albinism deafness syndrome, alkaptonuria, Alpers syndrome, alpha-1 antitrypsin deficiency, alpha-ketoglutarate dehydrogenase deficiency, alpha-mannosidosis, aminoacylase 1 deficiency, anemia sideroblastic and spinocerebellar ataxia, arginase deficiency, argininosuccinic aciduria, aromatic L-amino acid decarboxylase deficiency, arthrogryposis renal dysfunction cholestasis syndrome, Arts syndrome, aspartylglycosaminuria, atypical Gaucher disease due to saposin C deficiency, autoimmune polyglandular syndrome type 2, autosomal dominant optic atrophy and cataract, autosomal erythropoietic protoporphyria, autosomal recessive spastic ataxia 4, Barth syndrome, Bartter syndrome, Bartter syndrome antenatal type 1, Bartter syndrome antenatal type 2, Bartter syndrome type 3, Bartter syndrome type 4, Beta ketothiolase deficiency, biotinidase deficiency, Bjornstad syndrome, carbamoyl phosphate synthetase 1 deficiency, carnitine palmitoyl transferase 1A deficiency, carnitine-acylcarnitine translocase deficiency, carnosinemia, central diabetes insipidus, cerebral folate deficiency, cerebrotendinous xanthomatosis, ceroid lipofuscinosis neuronal 1, Chanarin-Dorfman syndrome, Chediak-Higashi syndrome, childhood hypophosphatasia, cholesteryl ester storage disease, chondrocalcinosisc, chylomicron retention disease, citrulline transport defect, congenital bile acid synthesis defect, type 2, Crigler Najjar syndrome, cytochrome c oxidase deficiency, D-2-hydroxyglutaric aciduria, D-bifunctional protein deficiency, D-glycericacidemia, Danon disease, dicarboxylic aminoaciduria, dihydropteridine reductase deficiency, dihydropyrimidinase deficiency, diabetes insipidus, dopamine beta hydroxylase deficiency, Dowling-Degos disease, erythropoietic uroporphyria associated with myeloid malignancy, Familial chylomicronemia syndrome, Familial HDL deficiency, Familial hypocalciuric hypercalcemia type 1, Familial hypocalciuric hypercalcemia type 2, Familial hypocalciuric hypercalcemia type 3, Familial LCAT deficiency, Familial partial lipodystrophy type 2, Fanconi Bickel syndrome, Farber disease, fructose-1,6-bisphosphatase deficiency, gamma-cystathionase deficiency, Gaucher disease, Gilbert syndrome, Gitelman syndrome, glucose transporter type 1 deficiency syndrome, glutamine deficiency, congenital, Glutaric acidemia. glutathione synthetase deficiency, glycine N-methyltransferase deficiency, Glycogen storage disease hepatic lipase deficiency, homocysteinemia, Hurler syndrome, hyperglycerolemia, Imerslund-Grasbeck syndrome, iminoglycinuria, infantile neuroaxonal dystrophy, Kearns-Sayre syndrome, Krabbe disease, lactate dehydrogenase deficiency, Lesch Nyhan syndrome, Menkes disease, methionine adenosyltransferase deficiency, mitochondrial complex deficiency, muscular phosphorylase kinase deficiency, neuronal ceroid lipofuscinosis, Niemann-Pick disease type A, Niemann-Pick disease type B, Niemann-Pick disease type C1, Niemann-Pick disease type C2, ornithine transcarbamylase deficiency, Pearson syndrome, Perrault syndrome, phosphoribosylpyrophosphate synthetase superactivity, primary carnitine deficiency, hyperoxaluria, purine nucleoside phosphorylase deficiency, pyruvate carboxylase deficiency, pyruvate dehydrogenase complex deficiency, pyruvate dehydrogenase phosphatase deficiency, yruvate kinase deficiency, Refsum disease, diabetes mellitus, Scheie syndrome, Sengers syndrome, Sialidosis Sjogren-Larsson syndrome, Tay-Sachs disease, transcobalamin 1 deficiency, trehalase deficiency, Walker-Warburg syndrome, Wilson disease, Wolfram syndrome, and Wolman disease.
Also provided herein is a computer readable medium comprising computer executable instructions configured to implement any of the methods described herein. In various embodiments, the computer readable medium is a non-transitory computer readable medium. In some embodiments, the computer readable medium is a part of a computer system (e.g., a memory of a computer system). The computer readable medium can comprise computer executable instructions for performing methods disclosed herein, such as methods for building, maintaining, implementing, and providing standardized solutions (e.g., DMSs and TSPs).
The methods described above, including the methods of building, maintaining, implementing, and providing standardized solutions (e.g., DMSs and TSPs) are, in some embodiments, performed on a computing device. Examples of a computing device can include a personal computer, desktop computer laptop, server computer, a computing node within a cluster, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, and the like.
The storage device 408 is a non-transitory computer-readable storage medium such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 406 holds instructions and data used by the processor 402. The input interface 414 is a touch-screen interface, a mouse, track ball, or other type of input interface, a keyboard, or some combination thereof, and is used to input data into the computing device 400. In some embodiments, the computing device 400 may be configured to receive input (e.g., commands) from the input interface 414 via gestures from the user. The graphics adapter 412 displays images and other information on the display 418. As an example, the display 418 can show a catalog of standardized solutions (e.g., DMSs and/or TSPs). The network adapter 416 couples the computing device 400 to one or more computer networks.
The computing device 400 is adapted to execute computer program modules for providing functionality described herein. As used herein, the term “module” refers to computer program logic used to provide the specified functionality. Thus, a module can be implemented in hardware, firmware, and/or software. In one embodiment, program modules are stored on the storage device 408, loaded into the memory 406, and executed by the processor 402.
The types of computing devices 400 can vary from the embodiments described herein. For example, the computing device 400 can lack some of the components described above, such as graphics adapters 412, input interface 414, and displays 418. In some embodiments, a computing device 400 can include a processor 402 for executing instructions stored on a memory 406.
In various embodiments, the different entities depicted in
The methods of building, maintaining, implementing, and providing TSPs and/or DMSs can be implemented in hardware or software, or a combination of both. In one embodiment, a non-transitory machine-readable storage medium, such as one described above, is provided, the medium comprising a data storage material encoded with machine readable data which, when using a machine programmed with instructions for using said data, is capable of perform the methods disclosed herein including methods of building, maintaining, implementing, and providing TSPs and/or DMSs. Embodiments of the methods described above can be implemented in computer programs executing on programmable computers, comprising a processor, a data storage system (including volatile and non-volatile memory and/or storage elements), a graphics adapter, an input interface, a network adapter, at least one input device, and at least one output device. A display is coupled to the graphics adapter. Program code is applied to input data to perform the functions described above and generate output information. The output information is applied to one or more output devices, in known fashion. The computer can be, for example, a personal computer, microcomputer, or workstation of conventional design.
Each program can be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. In any case, the language can be a compiled or interpreted language. Each such computer program is preferably stored on a storage media or device (e.g., ROM or magnetic diskette) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. The system can also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein.
The signature patterns and databases thereof can be provided in a variety of media to facilitate their use. “Media” refers to a manufacture that contains the signature pattern information of the present invention. The databases of the present invention can be recorded on computer readable media, e.g., any medium that can be read and accessed directly by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage medium, and magnetic tape; optical storage media such as CD-ROM; electrical storage media such as RAM and ROM; and hybrids of these categories such as magnetic/optical storage media. One of skill in the art can readily appreciate how any of the presently known computer readable mediums can be used to create a manufacture comprising a recording of the present database information. “Recorded” refers to a process for storing information on computer readable medium, using any such methods as known in the art. Any convenient data storage structure can be chosen, based on the means used to access the stored information. A variety of data processor programs and formats can be used for storage, e.g., word processing text file, database format, etc.
Disclosed herein is a method for characterizing a disease of a subject, the method comprising: obtaining a measurement of interest from the subject; selecting a target solution profile from a plurality of target solution profiles; and applying the target solution profile to the obtained measurement of interest to characterize the disease for the subject, wherein the target solution profile comprises: a measurement definition defining one or more subject changes relevant to the disease; an instrumentation asset that transforms the measurement of interest captured according to the measurement definition to a dataset, the instrumentation asset being device-agnostic and is thereby interchangeable across different target solution profiles, wherein the instrumentation asset is validated; and an interpretation asset aligned with the measurement definition, the interpretation asset configured to interpret the dataset from the instrumentation asset and characterize the disease of the subject.
In various embodiments, the target solution profile is previously validated by implementing one or more qualification protocols used to establish equivalency of solutions across the plurality of target solution profiles.
Additionally disclosed herein is a method for building a target solution profile for characterizing a disease, the method comprising: generating a measurement definition of the target solution profile that defines one or more subject changes relevant to the disease; selecting an instrumentation asset that transforms data captured according to the measurement definition to a dataset, the instrumentation asset being device-agnostic and is thereby interchangeable across different target solution profiles; and generating an interpretation asset of the target solution profile aligned with the measurement definition, the interpretation asset configured to interpret the dataset from the instrumentation asset and characterize the disease. In various embodiments, the method disclosed herein further comprises implementing a qualification protocol to validate the target solution profile, the qualification protocol used to establish equivalency of solutions across the plurality of target solution profiles. In various embodiments, the measurement definition and interpretation asset are fixed for the target solution profile and specific for the disease. In various embodiments, the instrumentation asset is interchangeable across different target solution profiles for characterizing different diseases. In various embodiments, the instrumentation asset is specific for a class of devices. In various embodiments, the class of devices comprises wearable devices (e.g., wrist-worn device), ingestibles, image and voice based devices, touchless sensors, and sensor based smart devices (e.g., scales, thermometers, and respirators). In various embodiments, the instrumentation asset comprises a machine learning model that transforms data captured according to the measurement definition to the dataset.
In various embodiments, the qualification protocol is implemented to validate equivalency of solutions across different classes of devices. In various embodiments, the target solution profile represents a standardized digital measurement solution for characterizing the disease. In various embodiments, the target solution profile comprises a measurement stack comprising a plurality of layers. In various embodiments, each of the measurement definition, the instrumentation asset, and the interpretation asset are represented by one or more layers in the plurality of layers. In various embodiments, an order of the plurality of layers comprises one or more layers of the measurement definition, one or more layers of the instrumentation asset, and one or more layers of the interpretation asset.
In various embodiments, a layer of the measurement definition interfaces with a layer of the instrumentation asset, and a layer of the instrumentation asset interfaces with the interpretation asset. In various embodiments, the one or more layers of the measurement definition comprise one or more of a hypothesis of the disease and a measurable concept of interest. In various embodiments, the one or more layers of the instrumentation asset comprise one or more of a measurement method, health data, and a machine learning algorithm. In various embodiments, the one or more layers of the interpretation asset comprise one or more of an analytical validation and a clinical interpretation. In various embodiments, the one or more layers of the measurement definition comprise a hypothesis of the disease and a measurable concept of interest, wherein the one or more layers of the instrumentation asset comprise a measurement method, health data, and a machine learning algorithm, and wherein the one or more layers of the interpretation asset comprise an analytical validation and a clinical interpretation.
In various embodiments, the disease is dementia. In various embodiments, the hypothesis comprises an intervention that slows progression of dementia. In various embodiments, the measurable concept of interest comprises one or more of attention, language, or executive functioning. In various embodiments, the measurement method comprises a method for capturing speech. In various embodiments, the health data comprises raw speech data captured from a subject or magnetic resonance imaging data. In various embodiments, the machine learning algorithm comprises one or more of a natural language processing algorithm, a clustering algorithm, and a clinical variable predictor. In various embodiments, the analytical validation comprises evidence validating performance of the machine learning algorithm. In various embodiments, the clinical interpretation comprises evidence supporting characterization of the disease according to a clinical dementia rating.
In various embodiments, the disease is Parkinson's Disease. In various embodiments, the hypothesis comprises an intervention that reduces tremor. In various embodiments, the measurable concept of interest comprises ability to perform daily activities of moderate intensity. In various embodiments, the measurement method comprises methods for capturing physiological data using a biosensor. In various embodiments, the health data comprises raw physiological data captured using a biosensor. In various embodiments, the machine learning algorithm transforms the health data to the dataset. In various embodiments, the analytical validation comprises evidence validating performance of the machine learning algorithm. In various embodiments, the clinical interpretation comprises evidence supporting characterization of the disease in a Parkinson's Disease patient population
Additionally disclosed herein is a method for providing one or more target solution profiles useful for characterizing one or more diseases, the method comprising: providing a catalogue comprising a plurality of target solution profiles, wherein each of one or more of the target solution profiles comprises: a measurement definition of the target solution profile defining one or more subject changes relevant to a disease of the one or more diseases; an instrumentation asset that transforms data captured according to the measurement definition to a dataset, the instrumentation asset being device-agnostic and is thereby interchangeable across different target solution profiles, wherein the instrumentation asset is validated by implementing one or more qualification protocols used to establish equivalency of solutions across a plurality of instrumentation assets; and an interpretation asset aligned with the measurement definition, the interpretation asset configured to interpret the dataset from the interchangeable instrumentation asset and characterize the disease of the subject; receiving, from a third party, a selection of one or more of the target solution profiles; providing the selected one or more target solution profiles to the third party.
In various embodiments, the target solution profile comprises a measurement stack comprising a plurality of layers. In various embodiments, each of the measurement definition, the instrumentation asset, and the interpretation asset are represented by one or more layers in the plurality of layers. In various embodiments, an order of the plurality of layers comprises one or more layers of the measurement definition, one or more layers of the instrumentation asset, and one or more layers of the interpretation asset. In various embodiments, a layer of the measurement definition interfaces with a layer of the instrumentation asset, and a layer of the instrumentation asset interfaces with the interpretation asset. In various embodiments, the one or more layers of the measurement definition comprise one or more of a hypothesis of the disease and a measurable concept of interest. In various embodiments, the one or more layers of the instrumentation asset comprise one or more of a measurement method, health data, and a machine learning algorithm. In various embodiments, the one or more layers of the interpretation asset comprise one or more of an analytical validation and a clinical interpretation. In various embodiments, the one or more layers of the measurement definition comprise a hypothesis of the disease and a measurable concept of interest, wherein the one or more layers of the instrumentation asset comprise a measurement method, health data, and a machine learning algorithm, and wherein the one or more layers of the interpretation asset comprise an analytical validation and a clinical interpretation.
In various embodiments, methods disclosed herein further comprise: receiving, from the third party, a search query; for each of one or more target solution profiles in the plurality of target solution profiles, evaluating the target solution profile to determine whether the target solution profile satisfies the query; and returning a list of target solution profiles that satisfy the query. In various embodiments, evaluating the target solution profile comprises: evaluating one or more layers of the measurement definition for a concept of interest that satisfies the query. In various embodiments, methods disclosed herein further comprise: in response to a request from the third party, replacing the instrumentation asset of the target solution profile with a second instrumentation asset to generate a revised target solution profile; and providing the revised target solution profile in the catalogue comprising the plurality of target solution profiles.
Additionally disclosed herein is a non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to obtain a measurement of interest from the subject; select a target solution profile from a plurality of target solution profiles; and apply the target solution profile to the obtained measurement of interest to characterize the disease for the subject, wherein the target solution profile comprises: a measurement definition defining one or more subject changes relevant to the disease; an instrumentation asset that transforms the measurement of interest captured according to the measurement definition to a dataset, the instrumentation asset being device-agnostic and is thereby interchangeable across different target solution profiles, wherein the instrumentation asset is validated; and an interpretation asset aligned with the measurement definition, the interpretation asset configured to interpret the dataset from the instrumentation asset and characterize the disease of the subject.
In various embodiments, the target solution profile is previously validated by implementing one or more qualification protocols used to establish equivalency of solutions across the plurality of target solution profiles.
Additionally disclosed herein is a non-transitory computer readable medium for building a target solution profile for characterizing a disease comprising instructions that, when executed by a processor, cause the processor to: generate a measurement definition of the target solution profile that defines one or more subject changes relevant to the disease; select an instrumentation asset that transforms data captured according to the measurement definition to a dataset, the instrumentation asset being device-agnostic and is thereby interchangeable across different target solution profiles; and generate an interpretation asset of the target solution profile aligned with the measurement definition, the interpretation asset configured to interpret the dataset from the instrumentation asset and characterize the disease.
In various embodiments, the non-transitory computer readable medium further comprises instructions that when executed by the processor, cause the processor to implement a qualification protocol to validate the target solution profile, the qualification protocol used to establish equivalency of solutions across the plurality of target solution profiles. In various embodiments, the measurement definition and interpretation asset are fixed for the target solution profile and specific for the disease. In various embodiments, the instrumentation asset is interchangeable across different target solution profiles for characterizing different diseases. In various embodiments, the instrumentation asset is specific for a class of devices. In various embodiments, the class of devices comprises wearable devices, ingestibles, image and voice based devices, touchless sensors, and sensor based smart devices (e.g., scales, thermometers, and respirators). In various embodiments, the instrumentation asset comprises a machine learning model that transforms data captured according to the measurement definition to the dataset.
In various embodiments, the qualification protocol is implemented to validate equivalency of solutions across different classes of devices. In various embodiments, the target solution profile represents a standardized digital measurement solution for characterizing the disease. In various embodiments, the target solution profile comprises a measurement stack comprising a plurality of layers. In various embodiments, each of the measurement definition, the instrumentation asset, and the interpretation asset are represented by one or more layers in the plurality of layers. In various embodiments, an order of the plurality of layers comprises one or more layers of the measurement definition, one or more layers of the instrumentation asset, and one or more layers of the interpretation asset.
In various embodiments, a layer of the measurement definition interfaces with a layer of the instrumentation asset, and a layer of the instrumentation asset interfaces with the interpretation asset. In various embodiments, the one or more layers of the measurement definition comprise one or more of a hypothesis of the disease and a measurable concept of interest. In various embodiments, the one or more layers of the instrumentation asset comprise one or more of a measurement method, health data, and a machine learning algorithm. In various embodiments, the one or more layers of the interpretation asset comprise one or more of an analytical validation and a clinical interpretation. In various embodiments, the one or more layers of the measurement definition comprise a hypothesis of the disease and a measurable concept of interest, wherein the one or more layers of the instrumentation asset comprise a measurement method, health data, and a machine learning algorithm, and wherein the one or more layers of the interpretation asset comprise an analytical validation and a clinical interpretation.
In various embodiments, the disease is dementia. In various embodiments, the hypothesis comprises an intervention that slows progression of dementia. In various embodiments, the measurable concept of interest comprises one or more of attention, language, or executive functioning. In various embodiments, the measurement method comprises a method for capturing speech. In various embodiments, the health data comprises raw speech data captured from a subject or magnetic resonance imaging data. In various embodiments, the machine learning algorithm comprises one or more of a natural language processing algorithm, a clustering algorithm, and a clinical variable predictor. In various embodiments, the analytical validation comprises evidence validating performance of the machine learning algorithm. In various embodiments, the clinical interpretation comprises evidence supporting characterization of the disease according to a clinical dementia rating.
In various embodiments, the disease is Parkinson's Disease. In various embodiments, the hypothesis comprises an intervention that reduces tremor. In various embodiments, the measurable concept of interest comprises ability to perform daily activities of moderate intensity. In various embodiments, the measurement method comprises methods for capturing physiological data using a biosensor. In various embodiments, the health data comprises raw physiological data captured using a biosensor. In various embodiments, the machine learning algorithm transforms the health data to the dataset. In various embodiments, the analytical validation comprises evidence validating performance of the machine learning algorithm. In various embodiments, the clinical interpretation comprises evidence supporting characterization of the disease in a Parkinson's Disease patient population
Additionally disclosed herein is a non-transitory computer readable medium for providing one or more target solution profiles useful for characterizing one or more diseases, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: provide a catalogue comprising a plurality of target solution profiles, wherein each of one or more of the target solution profiles comprises: a measurement definition of the target solution profile defining one or more subject changes relevant to a disease of the one or more diseases; an instrumentation asset that transforms data captured according to the measurement definition to a dataset, the instrumentation asset being device-agnostic and is thereby interchangeable across different target solution profiles, wherein the instrumentation asset is validated by implementing one or more qualification protocols used to establish equivalency of solutions across a plurality of instrumentation assets; and an interpretation asset aligned with the measurement definition, the interpretation asset configured to interpret the dataset from the interchangeable instrumentation asset and characterize the disease of the subject; receive, from a third party, a selection of one or more of the target solution profiles; provide the selected one or more target solution profiles to the third party. In various embodiments, the target solution profile comprises a measurement stack comprising a plurality of layers. In various embodiments, each of the measurement definition, the instrumentation asset, and the interpretation asset are represented by one or more layers in the plurality of layers. In various embodiments, an order of the plurality of layers comprises one or more layers of the measurement definition, one or more layers of the instrumentation asset, and one or more layers of the interpretation asset. In various embodiments, a layer of the measurement definition interfaces with a layer of the instrumentation asset, and a layer of the instrumentation asset interfaces with the interpretation asset. In various embodiments, the one or more layers of the measurement definition comprise one or more of a hypothesis of the disease and a measurable concept of interest. In various embodiments, the one or more layers of the instrumentation asset comprise one or more of a measurement method, health data, and a machine learning algorithm. In various embodiments, the one or more layers of the interpretation asset comprise one or more of an analytical validation and a clinical interpretation. In various embodiments, the one or more layers of the measurement definition comprise a hypothesis of the disease and a measurable concept of interest, wherein the one or more layers of the instrumentation asset comprise a measurement method, health data, and a machine learning algorithm, and wherein the one or more layers of the interpretation asset comprise an analytical validation and a clinical interpretation.
In various embodiments, the non-transitory computer readable medium further comprises instructions that, when executed by the processor, cause the processor to: receive, from the third party, a search query; for each of one or more target solution profiles in the plurality of target solution profiles, evaluate the target solution profile to determine whether the target solution profile satisfies the query; and return a list of target solution profiles that satisfy the query. In various embodiments, the instructions that cause the processor to evaluate the target solution profile further comprise instructions that, when executed by the processor, cause the processor to: evaluate one or more layers of the measurement definition for a concept of interest that satisfies the query. In various embodiments, the non-transitory computer readable medium further comprises instructions that, when executed by the processor, cause the processor to: in response to a request from the third party, replace the instrumentation asset of the target solution profile with a second instrumentation asset to generate a revised target solution profile; and provide the revised target solution profile in the catalogue comprising the plurality of target solution profiles.
The measurement stack is divided into nine individual layers. Each components includes unique information that can stand alone and offer additional value combined with the other components. These components have been categorized into three sub-stacks (otherwise referred to as assets): the definition-, instrumentation- and validation sub-stacks. The definition sub-stack describes the condition, meaningful aspect of health, and concepts of interest. Regulatory acceptance of this sub-stack is valuable for pursuing a study and should be aligned early on. The instrumentation sub-stack includes components for collecting, analyzing, and interpreting data. These layers include both specific solutions and generically described classes of solutions. Finally, the validation sub-stack describes the validation studies and regulatory approval to complete the solutions. Although stacks can be built starting from every component, the process often begins with available instrumentation or a medical condition. Atopic dermatitis (AD) is showcased here to walk through the measurement stack and an example target solution profile.
Component 2 in the measurement stack refers to the concept of interest. Here, the concept of interest (COI) describes how this specific meaningful aspect of health will be measured. For example, if the goal is to measure nocturnal scratching, the concept of interest can be any of a measure of total sleep time, scratching events per hour, or total scratching events. This COI is practically measured using an outcome to measure (OTM). For example, the outcome to measure includes the specific, measurable characteristics of the condition that evaluate the MAH described by the COI. Thus, the outcome to measure reveals whether the treatment of the MAH is beneficial. For example, reduction in scratching events after X weeks. Although not explicitly shown in
Component 3 in the measurement stack refers to the measurement method, which is part of the instrumentation asset. The measurement method includes hardware, software, or firmware solutions. Examples are wearable devices, mobile applications, and sensors. Additionally, complete software solutions can be a measurement method (e.g., speech batteries) as long as the method can reliably measure the OTM. As a specific example, a measurement method is a watch with a 3-axis MEMS accelerometer (10-100 Hz). This device captures accelerations 24/7 for 7 to 45 days (dependent on the set frequency).
Component 4 in the measurement stack refers to the raw data. This component represents the raw datasets which are the outcomes of the measurement method. Generally, raw data does not yet deliver meaningful health data. However, raw data is an individual layer as each of these datasets can be leveraged by multiple algorithms for different purposes. Here, the raw data layer is introduced as, for example, a raw file that provides 10-100 Hz accelerations. This is captured in 3D SI units (XYZ g-force) with 28 days of continuous data collection. In short, this example describes what raw data is captured (accelerations in 3D SI units), its frequency (10-100 Hz), and the amount of data that is captured (28 days).
Component 5 in the measurement stack refers to the algorithm. The algorithm represents a method in transforming raw data into meaningful health datasets. Thus, meaningful health datasets are readily analyzable and interpreted. Algorithms can be integrated into the complete instrumentation, or individual algorithms can be leveraged to transform (individual) raw datasets. For example, Philips Respironics RADA algorithm interprets measurement device data into sleep and scratching events.
Component 6 in the measurement stack refers to the health data. The health data component describes the health dataset generated by the algorithm from the initial raw datasets. Health datasets can be assessed as individual datasets for multiple purposes. Therefore, health datasets are considered a standalone asset and are included in the stack as an individual layer. For example, meaningful health datasets provide total sleep time, scratching events per hour, and the total number of scratching events.
Component 7 in the measurement stack refers to the technical and analytical validations. Further, component 8 in the measurement stack refers to clinical validation. The different validations ensure that digital measures are valid and therefore, can be recognized as eligible for clinical trial approval. Instrumentation outputs are evaluated both in silico and in vitro at the sample level. Specifically, technical validation (referred to as V1 in
The analytical validation (referred to as V2 in
The clinical validation (referred to as V3 in
Component 9 of the measurement stack refers to regulatory validation. Here, regulatory qualifications by health authorities are of importance for the acceptance and adoption of digital measures. The regulatory validation assesses all layers of the measurement stack, but predominantly is applied for the definition- and validation components. For example, regulatory precedence internal and external for the definition, instrumentation, and evidence.
As shown in
Component 3 (measurement method) in the DMS identifies the particular device that is used for capturing raw data. Specifically, as shown in
In the instrumentation asset, the measurement method generically identifies a wrist worn device with device specifications. Here, the measurement method is device-technology agnostic and does not identify a particular device nor a particular device-software. The raw data component includes the raw data file that is captured using the measurement method (e.g., wrist worn device). The algorithm component identifies algorithms that transform the raw data file captured using the measurement method into meaningful health data. The health data component includes the meaningful health dataset transformed by the algorithm of the preceding component. As shown in
The analytical validation component ensures that the meaningful health dataset is reliable, valid, and sensitive for the concept of interest. The clinical interpretation identifies a significant improvement in daily performance in PAH patients following the drug X intervention.
Referring to the DMS shown in
Referring to the DMS shown in
Each digital measurement solution shown in
As shown in
The interaction between regulators and the stakeholders can be as follows: at step 5, the regulator logs in, browses the DEEP catalogue to further understand the digital solution with additional context, and views the public questions as well as the private background materials from both sponsor companies with their private questions. If needed, the regulators can re-engage with the stakeholders to obtain additional clarity and information. The regulator sees that patient and clinician input has already been incorporated. However, the regulator still has questions e.g., it appears important that for severe forms of the disease a comprehensive assessment is made about scratch activity. Thus, regulator asks for additional context regarding patient behavior such as where the patients scratch, how do they scratch, the hours of scratching, etc. The regulators also want to ask patients with less severe disease and understand if their scratch activity is different. The stakeholders (e.g., patient representatives) provide this feedback. Here, patients with severe forms of the disease scratch everywhere and also use both hands and even their feet to scratch. Alternatively, patients with the less severe form of the disease report that usually their itch flares up in one area and they end up scratch just that one spot.
The regulator then wants to ask the patients with a severe form of the disease about the camera solution. In light of their disease, would they tolerate the use of such a solution for periods of time? The patient representative responds that yes, their disease is already burdensome so that they would be willing to do this in order to help find a solution. They do however express concerns about doing this for an extended period of time. The regulator considers all this input and then formulates their response to the questions. They agree that scratch indeed is very meaningful and can be used as a key endpoint.
Regarding the two solutions envisioned, the regulator sees good applicability for both in different kinds of trials. They both sound plausible, but evidence of their performance to detect scratch activity is required and they also recommend developing evidence to better understand how much change in this measure is going to result in a meaningful benefit to patients. Also, the impact on sleep and next day sleepiness should be explored.
Regarding the private questions, the regulator recommends to Pharma company A (severe disease) to consider using both envisioned solutions in their trials. A study could be designed with periods of camera observation as well as wearable device use. It could be studied if the wearable solution could be a suitable surrogate for the more robust video measures. Thus, if the additional value of the video solution can be better understood, better guidance can be provided in the future. An ideal solution could be to use both in studies with severe forms of the disease, balancing scientific value with the burden on patients.
For pharma company B, the regulator foresees that the single wearable device solution could be sufficient to measure these isolated scratching flares. The regulator however recommends that the company also works to understand how well the video and wearable measures correlate and then make an informed choice in their trial design. The regulator recommends the sponsor comes back for more advice when more evidence is available and then discuss specific trial designs again.
Thus, if the regulator sees sufficient evidence, at step 6, the regulator provides regulatory acceptance of the digital solution. At step 7, the collaborative mission involving the multiple stakeholders is completed. The regulatory feedback is curated and connected with the catalogue (MAH, COI and TSP). Stakeholders involved in this regulatory process now have clear direction about next steps. Both solutions have their uses for different purposes and both sponsor companies are already starting to plan for solution development missions. Pharma company A wants to invest in both options, Pharma company B is interested in both, but clearly wants to prioritize the wearable solution development first.
Assume that Pharmaceutical company A and Pharmaceutical Company B have generated their respective measurement definitions for atopic dermatitis and are interested in measuring number of nighttime scratching events. The remaining question is how to capture these measurements. Pharmaceutical company A would like to develop the right solution for measuring their endpoint. Thus Pharmaceutical company A accesses and searches the DEEP catalogue to identify the solutions that are already in existence.
For example, Pharmaceutical company A types into the asset search box: “scratch”, which results in discovering sensor devices, algorithms and relevant datasets for this use case. Thus, Pharmaceutical A can find the building blocks needed for their solution.
Here, Pharmaceutical company A can create a new mission seeking the services of a Custodian that can assemble a digital solution and maintain it. Pharmaceutical company A will fully fund this work, sets the access rights to Pharmaceutical company A fully owning the solution, but granting an operating license to the Custodian for a period of 3 years, with the Custodian being responsible for maintaining documentation of the solution, including any component upgrades during the licensing period.
The custodian assembles the solution from the components in the catalogue and connects the solution to the measurement definition already established. Pharmaceutical company A can now access the solution they need in their clinical trial.
Additionally, Pharmaceutical company B can now also see the solution being available for licensing from the Custodian (they get a notification that DMS is available for the TSP they are following/subscribed to). The solution performance looks promising and the licensing conditions appears fair. Pharmaceutical company B also decides to license the solution from the Custodian.
Altogether, multiple stakeholders can rapidly adopt solutions through more efficient pathways that are provided through standardized solutions.
Streptococcus, Staphylococcus, and Enterococcus as
| Number | Date | Country | Kind |
|---|---|---|---|
| 21383036.7 | Nov 2021 | EP | regional |
This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63/184,907 filed May 6, 2021 and EP21383036.7 filed Nov. 16, 2021, the entire disclosure of each of which is hereby incorporated by reference in its entirety for all purposes.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2022/062360 | 5/6/2022 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63184907 | May 2021 | US |
