Patent Application
20240363255
The present disclosure relates to the field of smart medical technologies, and in particular to an emergency treatment system, an emergency treatment method and an electronic device.
Emergency treatments refer to seeking emergency treatment help by making an emergency call or through other means when a patient suddenly becomes ill outside hospitals. It should be noted that the information disclosed in the Background section above is only for enhancing the understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
The present disclosure provides an emergency treatment system, an emergency treatment method and an electronic device.
According to an aspect of the present disclosure, there is provided an emergency treatment system, including:
According to an aspect of the present disclosure, there is provided an emergency treatment method, including:
It should be noted that the above general description and the following detailed description are merely exemplary and explanatory and should not be construed as limiting of the disclosure.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description serve to explain the principles of the present disclosure. Apparently, the drawings in the following description are only some embodiments of the present disclosure, and those skilled in the art can obtain other drawings according to these drawings without creative efforts.
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the embodiments can be implemented in a variety of forms and should not be construed as being limited to examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more complete and comprehensive so as to convey the idea of the example embodiments to those skilled in this art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
In addition, the drawings are merely schematic representations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and the repeated description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and/or processor devices and/or microcontroller devices.
It should be noted that the terms “include”, “configured with” and “provided” in the present disclosure are used to indicate a non-exclusive inclusion and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.
There is information isolation between hospitals and ambulance vehicles in conventional emergency treatments. In some instances, some emergency equipment equipped on the ambulance vehicles can provide medical data transmission functions, but there is still a lack of unified management capabilities for medical data.
In an emergency treatment system of embodiments of the present disclosure, scattered medical data of a subject to be treated is transmitted to a data lake cluster for integration, thereby enabling storage of structured medical data, semi-structured medical data and unstructured medical data at any scale. In addition, various roles in the emergency treatment system, such as medical staff, data developers, and business analysts, are allowed to access data through analysis tools and frameworks of their choice, so as to collaboratively process and analyze the data in different manners. In turn, the emergency treatment system in embodiments of the present disclosure can support a medical institution to provide a more precise treatment plan for the subject to be treated.
In addition to transporting the medical staff and the subject to be treated, the ambulance vehicle 101 is usually used to deploy vehicle-mounted emergency medical instruments. For example, the vehicle-mounted emergency medical instruments may include: a defibrillation monitor, a ventilator, a non-invasive multi-parameter detector (MTX), a blood pressure testing device, a body fat testing device, a blood glucose testing device, a sleep monitoring device, an electric suction device, a negative pressure system, an oxygen supply device, a sterilization device, a lighting device, an exhaust system, a heating system, a power supply line system, an automatic onboard stretcher system, etc. Some vehicle-mounted emergency medical instruments are capable of collecting medical data of the subject to be treated, that is, these vehicle-mounted emergency medical instruments serve as the information collection terminal as described below. The information collection terminal may include, for example, the above-mentioned ventilator, non-invasive multi-parameter detector (MTX), blood pressure testing device, body fat detection device, blood glucose testing device, sleep monitoring device, etc. In addition, some electronic devices of some subjects to be treated, such as a wearable device or other mobile terminals with a medical data collection function, may also be regarded as the information collection terminal described in embodiments of the present disclosure.
The information collection terminal collects the medical data of the subject to be treated and uploads it to the data lake cluster 102. The data lake cluster 102 is used for data access, data processing, data storage and data distribution of the medical data of the subject to be treated. In embodiments of the present disclosure, the data lake cluster 102 may be, for example, a data storage and management service system composed of multiple types of databases, including a relational database such as MariaDB, MySQL, etc., a document database such as MongoDB, CouchDB, etc., a distributed file system such as HDFS, PVFS, PanFS, etc., and a graph database such as Neo4j, Cayley, rapgDB, etc. In embodiments of the present disclosure, the data lake cluster 102 may adopt a distributed computing and storage architecture, integrate various types of computer stand-alone machines, servers and computer clusters or server clusters with data storage and computing functions, and provide various types of functional components including data management and algorithm development. Through the data lake cluster 102, it is possible to store the structured medical data, the semi-structured medical data and the unstructured medical data at any scale without the need for structured processing of the medical data, allow various roles in the emergency treatment system 100, such as the medical staff, the data developers, and the business analysts, to access the data through the analysis tools and frameworks of their choice, so as to collaboratively process and analyze the data in different manners.
In embodiments of the present disclosure, the medical business system 103 can obtain the medical data of the subject to be treated from the data lake cluster 102. For example, the medical business system 103 may be deployed in a medical institution such as the hospital for use by the medical staff.
In the emergency treatment system of embodiments of the present disclosure, the scattered medical data of the subject to be treated is transmitted to the data lake cluster for integration, thereby enabling storage of the structured medical data, the semi-structured medical data and the unstructured medical data at any scale. In addition, various roles in the emergency treatment system, such as the medical staff, the data developers, and the business analysts, are allowed to access the data through the analysis tools and frameworks of their choice, so as to collaboratively process and analyze the data in different manners. In turn, the emergency treatment system in embodiments of the present disclosure can support the medical institution to provide the more precise treatment plan for the subject to be treated.
The emergency treatment system of embodiments of the present disclosure is introduced in more detail below.
Referring to
The data access module 201 is configured to determine a corresponding target access manner according to a type of the information collection terminal and access the medical data of the subject to be treated in the target access manner.
In embodiments of the present disclosure, types of respective information collection terminals and corresponding target access manners may be configured and stored in advance. When a specific information collection terminal accesses, its corresponding target access manner is determined based on preconfigured information. For example, as shown in
The data processing module 202 is configured to analyze indicator information of the medical data of the subject to be treated, and establish a mapping relationship between each piece of indicator information and a standard indicator.
The indicator information is information related to a user's physiological indicator collected by the information collection terminal. In some implementations, the indicator information may include an indicator name and an indicator value.
Specifically, since communication protocols and interfaces used by different types of information collection terminals, data formats collected by different types of information collection terminals, etc. may be different, they need to be analyzed first. In some implementations, analyzing the indicator information includes analyzing the indicator value in the indicator information. For example, as shown in
In order to improve an analyzing efficiency of the indicator information of the medical data of the subject to be treated and reduce pre-configuration work for the analyzing manner, reused parts of analyzing manners of some information collection terminals are also extracted for modeling in embodiments of the present disclosure. In some implementations, the indicator information of the medical data of the subject to be treated uploaded by the information collection terminal is analyzed based on a data analysis model. The data analysis model is obtained by performing modeling based on an analytical protocol corresponding to the information collection terminal. In some implementations, analyzing in the data processing module the indicator information of the medical data of the subject to be treated may include: obtaining analytical protocols corresponding to a plurality of information collection terminals, and performing modeling based on an analytical protocol corresponding to each information collection terminal to obtain a data analysis model that is at least partially universal; and analyzing, based on the data analysis model, the indicator information of the medical data of the subject to be treated uploaded by the information collection terminal to which the data analysis model is applicable.
In an embodiment of the present disclosure, the indicator information of the medical data of the subject to be treated includes n fields, and the i-th field represents the (i−1)*m+1-th bit to the i*m-th bit of a binary value of an indicator of the medical data of the subject to be treated, where m is a positive integer and may be determined according to a conversion of number systems relationship. The data analysis model analyzes the indicator information of the medical data of the subject to be treated through the following formula:
where the function f1(x) is used to convert an object into a binary value, and f2(x) is used to convert the object into a decimal value.
For example, indicator information sent by a blood pressure testing device is a string 0x00,0x78,0x00,0x50,0x4B. In a data interface protocol provided by a manufacturer of the blood pressure testing device, meanings represented by characters in the string data have been defined. Reference is made to Table 1 below:
where, SYS[15:8] represents upper eight bits of the systolic blood pressure, and SYS[7:0] represents lower eight bits of the systolic blood pressure; DIA[15:8] represents upper eight bits of the diastolic blood pressure, and DIA[7:0] represents lower eight bits of the diastolic blood pressure; and PUL[7:0] represents lower eight bits of the pulse rate. Taking the systolic blood pressure as an example, the systolic blood pressure includes two fields (i.e. 0x00, 0x78), where the first field represents the 1st to 8th bits of a binary value of the systolic blood pressure; in this case, m=8, and then a data analysis model corresponding to the systolic blood pressure is:
Then, the function f1(x) is used to convert SYS[7:0], that is, 0x78, into the binary value 01111000, and the function f2(x) is used to convert 01111000 into the decimal value 120. Likewise, it may be obtained that a decimal value corresponding to SYS[15:8], that is, 0x00, is 0. As a result, the systolic blood pressure y=0*2561+120*2560=120 mmHg. Similarly, it may be obtained that the diastolic blood pressure is 80 mmHg and a pulse rate is 75 beats/minute. It should be noted that, those skilled in the art can easily understand that in other embodiments of the present disclosure, other data analysis models may also be obtained based on different interface protocols of the information collection terminals, which also fall within the protection scope of the present disclosure.
With continued reference to
In some embodiments, the standard indicator at least includes a standard code identity.
After the indicator information of the medical data of the subject to be treated uploaded by the information collection terminal is analyzed, a non-standard code identity therein is obtained.
Based on the mapping relationship between the non-standard code identity and the standard code identity, the standard code identity and the indicator value in the indicator information of the medical data of the subject to be treated are key-value stored.
The standard code identity corresponding to the medical data of the subject to be treated is associated with a device identity of the information collection terminal.
In some embodiments, a process of configuring the standard indicator may further be included.
Referring to
In the step S501, the standard indicator is configured, and the standard indicator at least includes the standard code identity.
For example, a standard indicator classification may be created, and is associated with a definition of a specific standard indicator in embodiments of the present disclosure. As an example, standard indicators for the blood pressure and the blood glucose are as follows.
In the step S502, the non-standard code identity of the indicator information of the medical data of the subject to be treated uploaded by the information collection terminal is obtained, and the mapping relationship between the non-standard code identity and the standard code identity is established.
For example, device information of the information collection terminal may be obtained in an embodiment, which may include, for example, basic information (such as a product name, a product type, a product model, a manufacturer name, a product picture), configuration information (such as a communication protocol, a data address, a data format, an interface protocol, an indicator rule, a time rule), etc. Then, the type of the information collection terminal may be determined based on the information such as the product type and the product model; the data uploaded by the information collection terminal may be obtained based on the information such as the communication protocol and the data address; the non-standard code identity may be analyzed from the uploaded data based on the information such as the data format, and the non-standard code identity is, for example, the indicator name in the indicator information; and then the mapping relationship between the non-standard code identity and the standard code identity may be established. For example, as shown in
In the step S503, after the indicator information of the medical data of the subject to be treated uploaded by the information collection terminal is analyzed, the non-standard code identity therein is obtained, and the standard code identity and the indicator value of the medical data of the subject to be treated are key-value stored based on the mapping relationship between the non-standard code identity and the standard code identity. For example, after the indicator information is analyzed, it is obtained that the systolic blood pressure SYS=120 mmHg and the diastolic blood pressure DIA=80 mmHg. A standard code identity corresponding to a non-standard code identity SYS is XY01, and a standard code identity corresponding to a non-standard code identity DIA is XY02, then the indicator information is stored as [XY01:120] and [XY02:80] in a key-value form.
In the step S504, the key-value stored indicator information of the medical data of the subject to be treated is associated with the device identity of the information collection terminal. In embodiments of the present disclosure, when the data uploaded by the information collection terminal is received, the device information of the information collection terminal is first matched with device information pre-collected in the above step S502, thereby ensuring that the indicator information of the medical data of the subject to be treated is in a one-to-one correspondence with each information collection terminal.
In some implementations, the standard indicator configuration process and the mapping relationship pre-building process involved in the above steps S501 and S502 may be omitted, and an execution order of the steps S503 and S504 is not limited.
In addition, with continued reference to
The data storage module 203 is configured to store the medical data of the subject to be treated. In some embodiments, the data storage module 203 may further be configured to store the standard indicator and the corresponding indicator information.
In embodiments of the present disclosure, the data storage module 203 may use the relational database such as MariaDB, MySQL, etc., the document database such as MongoDB, CouchDB, etc., the distributed file system such as HDFS, PVFS, PanFS, etc., and the graph database such as Neo4j, Cayley, rapgDB, etc. In addition, some data may be written into a distributed cache such as Redis, Memcached, SSDB, etc. according to a data call frequency and a data type, thereby improving a reading speed.
The data distribution module 204 is configured to distribute the medical data of the subject to be treated processed by the data processing module to the medical business system.
In embodiments of the present disclosure, a second message platform 206 is further configured between the data distribution module 204 and the medical business system 103. The second message platform 206 is used by the data distribution module 204 to transmit the medical data of the subject to be treated to the medical business system 103 in the message service manner. Then, the data distribution module 204 writes the medical data of the subject to be treated into the message queue such as RabbitMQ, ActiveMQ, ZeroMQ, Kafka, MetaMQ, RocketMQ, etc. The medical business system 103 may pull data from the message queue in the first-in-first-out manner for processing. By configuring the second message platform between the data distribution module and the medical business system, decoupling between the data distribution module and the medical business system is achieved, and the asynchronous processing between the data distribution module and the medical business system is facilitated. Furthermore, through the second message platform, data peak reduction and flow control can also be achieved, and the data is buffered to avoid network transmission congestion due to the large amount of data.
In addition, in some embodiments of the present disclosure, the data distribution module may further be configured to provide the medical data of the subject to be treated to the second message platform 206 in a HTTP transmission manner, a RSA encrypted transmission manner, a message queue manner, etc. For example, the data distribution module 204 writes the medical data of the subject to be treated into the message queue to facilitate the second message platform 206 to pull the medical data of the subject to be treated from the message queue.
In some embodiments, specific positions for providing the first message platform and the second message platform are not limited. For example, they may also be provided at other positions other than the position between the data access module and the data processing module and the position between the data distribution module and the medical business system.
In some implementations, the first message platform and the second message platform may be implemented by the same message platform. There is no limitation on the number of message platforms.
Referring to
In some embodiments, the management terminal 701 includes a device parameter monitoring module 705. The device parameter monitoring module 705 is configured to record and analyze behavior information of the information collection terminal to determine whether the information collection terminal is an abnormal terminal. In an embodiment of the present disclosure, the behavior information of the information collection terminal may include a usage time, the number of exceptions, etc. For example, the device parameter monitoring module 705 may record behavior information of a normal information collection terminal, such as the usage time, the number of exceptions, etc., and perform statistical analysis based on this to determine a fluctuation range of the behavior information of the normal information collection terminal, such as the usage time, the number of exceptions, etc. As a result, when it is monitored that behavior information of a certain information collection terminal, such as the usage time, the number of exceptions, etc., exceeds the above fluctuation range, this information collection terminal may be considered as the abnormal terminal.
In other embodiments of the present disclosure, sample data may also be obtained based on historical behavior information of the information collection terminal, so that one or more machine learning models, such as a random forest model, a deep neural network model, a support vector machine model, a boosted tree model, a general linear model and a progressive gradient regression tree model, are trained based on the sample data, so as to obtain an abnormal terminal determination model. In turn, the device parameter monitoring module 705 may input behavior information of a certain information collection terminal into the abnormal terminal determination model, so as to output, by means of the abnormal terminal determination model, a probability that the information collection terminal is the abnormal terminal. When a probability value that the information collection terminal is the abnormal terminal exceeds a threshold value, the information collection terminal may be considered to be the abnormal terminal.
In some embodiments of the present disclosure, the management terminal may further be configured to record the device identity and a device start of use time of the information collection terminal.
In some embodiments, the management terminal may further:
The medical business system is further configured to receive the medical data associated with the user information of the subject to be treated.
The process of recording the device identity, the device start of use time and the end of use time of the information collection terminal may be completed by the data processing module in the data lake cluster and then sent to the management terminal, or the management terminal may directly complete the above recording process.
For example, information obtained after the medical data collected by the information collection terminal is associated with the device identity, the device start of use time and the end of use time is as shown in Table 3 below:
The management terminal receives the medical data distributed from the data lake cluster, and the medical data at least includes the device identity, the device start of use time and the indicator information.
In some embodiments, the user information includes an identity of a user of the information collection terminal, which may include, for example, at least one of name, ID number, etc. Since data stored in the data lake cluster does not include the user information of the user, which may play a role of desensitizing the user information. When relevant medical data of the user is sent to the medical business system, the user information needs to be associated with the medical data.
In some embodiments, the user information may be obtained when the user makes an alarm through an alarm terminal and/or user's personal information is entered through a doctor terminal in the ambulance vehicle, and a time for making the alarm and/or entering the personal information on the doctor terminal may also be recorded at the same time. The user's user information and the recording time may be sent to the management terminal. According to the device identity and the device start of use time, the time corresponds to the user that obtains the personal information, thereby completing the association of the medical data with the user information of the subject to be treated. The medical data associated with the user information of the subject to be treated (that is, the medical data carrying the user information) is sent to the medical business system.
For example, the management terminal obtains: the user identity UserID: 123321; the device identity SN: 2820; the device start of use time BindingTimeStart: 2021-03-10 05:22:01; the device end of use time Binding TimeEnd: 2021-03-10 05:36:01.
In turn, after obtaining the information in Table 3 sent by the data lake cluster, the management terminal may, according to the device identity, the device start of use time and the end of use time in Table 3, perform matching for a time when the management terminal obtains the user information to obtain the user identity for the information in Table 3, and bind the data in Table 3 with the user identity. Therefore, through the above method, the data lake cluster does not need to store the user identity information, but only stores the device information and the medical data, thereby realizing the function of desensitizing the user identity information.
In some embodiments of the present disclosure, the management terminal may further be configured to store historical medical data of the subject to be treated, and jointly distribute the historical medical data and real-time medical data of the subject to be treated to the medical business system. Specifically, the management terminal may obtain the user identity corresponding to the device identity of the information collection terminal; thus, it may also obtain the historical medical data of the subject to be treated corresponding to the user identity through the user identity, and jointly distribute the historical medical data and the real-time medical data of the subject to be treated to the medical business system, which enables a user of the medical business system (such as a medical worker) to more comprehensively understand information of the subject to be treated.
In some embodiments of the present disclosure, the management terminal may further be configured to store medical data of relatives and friends of the subject to be treated, and jointly distribute the medical data of the relatives and friends of the subject to be treated and the medical data of the subject to be treated to the medical business system. Specifically, the management terminal stores the user identity corresponding to the device identity of the information collection terminal. Therefore, an identity of a user who has a relationship of relatives and friends with the user may also be obtained through the identity of the user of the current information collection terminal. Then, the medical data of the relatives and friends of the subject to be treated is obtained in the management terminal through the identity of the relatives and friends, and the medical data of the relatives and friends of the subject to be treated and the medical data of the subject to be treated are jointly distributed to the medical business system, thereby enabling the user of the medical business system (such as the medical worker) to understand genetic disease information or infectious disease information of the subject to be treated, etc.
With the continued reference to
Referring to
In the step S801, when a query subject is determined to be a target subject according to a query condition, a mark identity is generated for the target subject, and the mark identity is associated with a device identity of an information collection terminal corresponding to the target subject.
For example, in an embodiment of the present disclosure, if a medical institution needs to obtain medical data of the target subject who meets the following query condition: in a specified area, gender is female, age is greater than 50 years old, the blood diastolic blood pressure is greater than 90 mmHg, and the blood systolic blood pressure is greater than 140 mmHg, the data query module may determine whether the query subject satisfies the above query condition. If the query condition is satisfied, the query subject will be regarded as the target subject. Then the mark identity may be generated for the information collection terminal corresponding to the target subject, for example, an attribute identity of Select=1 is generated to mark the query subject as the target subject. Correspondingly, if the query condition is not satisfied, an attribute identity of Select=0 may be generated to mark the query subject as not the target subject.
In the step S802, it is monitored whether medical data of the target subject is received based on the device identity of the information collection terminal associated with the mark identity.
For example, the data query module generates, according to the mark identity, a mark identity generation time and the device identity corresponding to the target subject, a monitoring rule “device identity SN: A123456, mark identity generation time Binding_Time: 2020-09-08-12:12:12, mark identity Select: 1, diastolic blood pressure: greater than 90 mmHg, systolic blood pressure: greater than 140 mmHg”, and determines whether an information collection terminal corresponding to newly accessed medical data satisfies the monitoring rule; and if monitoring rule is satisfied, determines that the medical data of the target subject has been received.
In the step S803, when it is monitored that the medical data of the target subject is received, the medical data of the target subject is distributed to the medical institution corresponding to the query request.
With the continued reference to
In some implementations, the processes in the data lake cluster such as the data access, the data processing, the data distribution may be displayed to the user by the data visualization module 707, so as to facilitate the user to monitor each process. Moreover, the stored medical data may also be displayed by the data visualization module 707.
For example, referring to
In some implementations, the data interaction module 901 may send a request to the data visualization module 707 of the management terminal, so as to facilitate the data visualization module 707 to generate the visual content according to the data in the data lake cluster. In turn, after receiving the visual content, the data interaction module 901 may display it to the user through the front-end page. In addition, in some embodiments, the data interaction module 901 may also actively push a message to the user, such as a device usage reminder message, a health reminder message, etc.
The medical business system 203 can obtain the medical data of the subject to be treated from the data lake cluster.
In some embodiments of the present disclosure, there is a data conversion relationship between the medical data of the subject to be treated accessed by the data access module and the processed medical data of the subject to be treated distributed by the data distribution module. In some implementations, an input indicator includes a hypopnea index x1, an apnea index x2 and a pressure parameter x3, an output indicator includes a sleep apnea hypopnea index mean y1, and the following data conversion relationship exists between y1 and x1, x2 and x3:
For example, the data conversion relationship is: y1=w1*x1+w2*x2+w3*x3; where, w1, w2, and w3 are relational parameters.
For example, a value range of w1 is [1,1.00000011], a value range of w2 is [1,1.00000011], and a value range of w3 is [7.85046229E-18, 7.85046229E-16].
For example, a value of w1 is 1.00000011, a value of w2 is 1.00000011, and a value of w3 is 7.85046229E-17.
In some embodiments, the input indicator includes the hypopnea index x1, the apnea index x2 and the pressure parameter x3, the output indicator is an apnea index mean y2, and the following data conversion relationship exists between y2 and x1, x2 and x3:
For example, the data conversion relationship is: y2=w1*x1+w2*x2+w3*x3; where, w1, w2, and w3 are relational parameters.
For example, the value range of w1 is [5.55500801E-08, 0.50000006], the value range of w2 is [0.50000006, 1.00000006], and the value range of w3 is [2.22044605E-17, 2.22044605E-15].
For example, the value of w1 is 0.50000006, 0.500000056 or 5.55500801E-08, the value of w2 is 0.50000006 or 1.00000006, and the value of w3 is 2.22044605E-16.
In some embodiments, the input indicator includes the hypopnea index x1, the apnea index x2 and the pressure parameter ×3, the output indicator includes a hypopnea index mean y3, and the following data conversion relationship exists between y3 and x1, x2 and x3:
For example, the data conversion relationship is: y3=w1*x1+w2*x2+w3*x3; where, w1, w2 and w3 are relational parameters.
For example, the value range of w1 is [0.500000056, 1.00000006], the value range of w2 is [5.55500802E-08, 0.50000006], and the value range of w3 is [−7.85046229E-16, −7.85046229E-18].
For example, the value of w1 is 0.50000006, 0.500000056 or 1.00000006, the value of w2 is 5.55500802E-08 or 0.50000006, and the value of w3 is −7.85046229E-17.
In some embodiments of the present disclosure, the data conversion relationship between the medical data of the subject to be treated accessed by the data access module and the processed medical data of the subject to be treated distributed by the data distribution module is obtained in the following manner.
The machine learning model may be trained using sample medical data and sample business data to obtain the data conversion relationship. For example, the data conversion relationship may be obtained through a neural network model, such as a convolutional neural network model, a residual neural network model, a recurrent neural network model, a long short-term memory neural network model, etc. The data conversion relationship may also be obtained through a collaborative filtering model, a hidden Markov model, a conditional random field model, etc.
Referring to
In the step S1001, an original mapping matrix R between the sample medical data and the sample business data is obtained. The original mapping matrix R is an m×n matrix, and an element R(i,j) is used to characterize an relationship between an input indicator i in the sample medical data and an output indicator j in the sample business data. M and n are integers greater than 1, i, j, and k are positive integers, and i∈[1,m], j∈[1,n], k∈[1,m−1].
For example, sample medical data uploaded to the data lake cluster by an information collection terminal includes 9 input indicators, and sample business data generated by the data lake cluster based on medical data provided by an information collection terminal includes 10 output indicators. An original mapping matrix R between the sample medical data and the sample business data is for example shown in Table 4 below.
The original mapping matrix R is a 9×10 matrix, and the element R(i,j) is used to characterize the relationship between the input indicator i in the sample medical data and the output indicator j in the sample business data. For example, a value of the element R(1,1) is 1, which means that there is a correlation between the input indicator IN-01 and the output indicator OUT-01. A value of element R(1,2) is 0, which means that whether there is a correlation between the input indicator IN-01 and the output indicator OUT-02 cannot be determined. Taking the output indicator OUT-01 as the sleep apnea hypopnea index mean, IN-01 as the hypopnea index, and IN-06 as the apnea index as an example, it is currently known that the sleep apnea hypopnea index mean is related to the hypopnea index and the apnea index. For example, the sleep apnea hypopnea index mean is the sum of the hypopnea index and the apnea index, and thus, values of R(1,1) and R(6,1) are 1. However, whether there are correlations between the sleep apnea hypopnea index mean and other input indicators cannot be determined at present (that is, there may or may not be the correlation), and thus, values of R(2,1), R(3,1), etc. are all 0.
In the step S1002, for the number of dimensions k, a first matrix P of m×k and a second matrix Q of k×n are obtained with P×Q=R as a target, and a target mapping matrix Rk is calculated based on the first matrix P and the second matrix Q, where k is a positive integer, and k∈[1,m−1].
In embodiments of the present disclosure, hidden information mining may be performed based on the original mapping matrix to obtain the first matrix and the second matrix, so that the first matrix represents a degree of influence of the input indicator on each output indicator, and the second matrix represents a degree of the output indicator being influenced by each input indicator. Then, a third matrix is obtained by multiplying the first matrix and the second matrix, which includes a correlation between each input indicator and each output indicator. That is, for the element in the above Table 4 where it is impossible to determine whether there is the correlation between the input indicator and the output indicator, whether there is the correlation between the input indicator and the output indicator can be determined through the third matrix.
In some implementations, a first matrix P of m×m, a second matrix Q of n×n, and an intermediate matrix S of m×n may also be obtained with R=P×S×Q as the target, and the target mapping matrix Rk is calculated based on the first matrix P, the second matrix Q and the intermediate matrix S.
In some implementations, the first matrix P of m×m, a second matrix Q of m×n, and an intermediate matrix S of m×m may also be obtained with R=P×S×Q as the target, and the target mapping matrix Rk is calculated based on the first matrix P, the second matrix Q and the intermediate matrix S.
Specific dimensions of matrices P, S, and Q may be adjusted as needed, which are not specifically limited here.
Taking the original mapping matrix R in the above Table 4 as an example, in an embodiment of the present disclosure, singular value decomposition may be first performed on the original mapping matrix R to obtain a singular value Σ[1.73205081,1,1,1,1,1,1,1,1] of the original mapping matrix R, and the first matrix P and the second matrix Q may be obtained at the same time, for example, the first matrix P of 9×9 and the second matrix Q of 10×10 are obtained.
For example, when the number of dimensions k is 8, the intermediate matrix S is constructed through the first 8 values of the singular value Σ. The intermediate matrix S is a 9×10 matrix as shown in Table 5 below:
Specifically, the original mapping matrix R may be decomposed to obtain the first matrix P and the second matrix Q. For example, Python is used to call a function such as linalg.svd to decompose the original mapping matrix R, or the original mapping matrix R is decomposed in other ways. In some implementations, the singular value Σ may also be obtained.
In some implementations, after the first matrix P and the second matrix Q are obtained, and the target mapping matrix Rk is calculated according to the product of the first matrix P and the second matrix Q.
In some embodiments, the target mapping matrix Rk is calculated according to the product of the first matrix P, the second matrix Q, and the intermediate matrix S.
The resulting target mapping matrix Rk is shown in Table 6 below:
Similarly, when the number of dimensions k is 5, the intermediate matrix S is constructed through the first five values of the singular value Σ. The intermediate matrix S is a 9×10 matrix as shown in Table 7 below:
Specifically, the original mapping matrix R may be decomposed to obtain the first matrix P and the second matrix Q. For example, Python is used to call a function such as linalg.svd to decompose the original mapping matrix R, or the original mapping matrix R is decomposed in other ways. In some implementations, the singular value Σ may also be obtained.
In some implementations, after the first matrix P and the second matrix Q are obtained, and the target mapping matrix Rk is calculated according to the product of the first matrix P and the second matrix Q.
In some embodiments, the target mapping matrix Rk is calculated according to the product of the first matrix P, the second matrix Q, and the intermediate matrix S.
The resulting target mapping matrix Rk is shown in Table 8 below:
Similarly, when the number of dimensions k is 1, the intermediate matrix S is constructed through the first value of the singular value Σ. The intermediate matrix S is a 9×10 matrix as shown in Table 9 below:
Specifically, the original mapping matrix R may be decomposed to obtain the first matrix P and the second matrix Q. For example, Python is used to call a function such as linalg.svd to decompose the original mapping matrix R, or the original mapping matrix R is decomposed in other ways. In some implementations, the singular value Σ may also be obtained.
In some implementations, after the first matrix P and the second matrix Q are obtained, and the target mapping matrix Rk is calculated according to the product of the first matrix P and the second matrix Q.
In some embodiments, the target mapping matrix Rk is calculated according to the product of the first matrix P, the second matrix Q, and the intermediate matrix S.
The resulting target mapping matrix Rk is shown in Table 10 below:
In other embodiments of the present disclosure, the first matrix and second matrix may also be determined in other ways. For example, in an embodiment of the present disclosure, the first matrix and the second matrix may also be determined through a gradient descent algorithm combined with a preset loss function. In more embodiments of the present disclosure, it is also possible to determine the first matrix and the second matrix through the neural network model, which is not particularly limited in embodiments of the present disclosure.
In the step S1003, a data conversion relationship between each input indicator and an output indicator in the business data is determined according to the original mapping matrix and at least one target mapping matrix.
In embodiments of the present disclosure, the data conversion relationship between each input indicator and the output indicator in the business data may be determined based on the original mapping matrix and the 1st to (n−1)-th target mapping matrices. Alternatively, in other embodiments of the present disclosure, in order to reduce the amount of calculations, the data conversion relationship may also be determined according to the original mapping matrix and a smaller number of target mapping matrices (for example, the 2nd to (n−2)-th target mapping matrices, target mapping matrices with odd sequence numbers, target mapping matrices with even sequence number, etc.), which is not particularly limited in embodiments of the present disclosure.
Taking the determination of the data conversion relationship based on the original mapping matrix and the 1st to 8th target mapping matrices as an example, parameters in the 1st to 8th target mapping matrices may be summarized to obtain Table 11 below.
Data that does not need to be processed in the Table 11 is removed to obtain Table 12 below.
In embodiments of the present disclosure, the data conversion relationship may be:
where, yj is a value of an output indicator j, a parameter of the function f is determined based on the original mapping matrix and element values of a column where the output indicator j is located in at least one target mapping matrix, and xi is a value of an input indicator i. For example, for a value of an output indicator y1 (that is, OUT-01) in the above Table 12, its data conversion relationship with values of an input indicator x1 (that is, IN-01), an input indicator x2 (that is, IN-06), and an input indicator x3 (that is, IN-07) is: y1=f(x1,x2,x3). Similarly, the correlation between other input indicators and output indicators may be determined. A method for determining the parameter of the function f will be explained below with reference to examples.
For example, the input indicator IN-01 is the hypopnea index x1, the input indicator IN-06 is the apnea index x2, and the input indicator IN-07 is the pressure parameter x3, the output indicator OUT-01 is the sleep apnea hypopnea index mean y1, and a conversion mathematical relationship corresponding to the sleep apnea hypopnea index mean is:
y
1
=f
1(x1,x2,x3)
where, a parameter of the function f1 is determined based on the original mapping matrix and element values of a column (i.e., column 3) where the sleep apnea hypopnea index mean is located in at least one target mapping matrix (the 1st to 8th target mapping matrices are taken as an example here). For example, a relationship between the sleep apnea hypopnea index mean and the hypopnea index, the apnea index and the pressure parameter is a weighted sum, and the corresponding conversion relationship may be specifically:
where, w1, w2 and w3 are determined based on the original mapping matrix and element values of a column where the sleep apnea hypopnea index mean is located in at least one target mapping matrix. For example, in the above Table 12, element values in a third column of a row where the input indicator IN-01 is located are respectively: 1.00000011, 1.00000011, 1.00000011, 1.00000011, 1, 1.00000011, 1.00000011, 1.00000011, and then a value range of w1 may be [1, 1.00000011]. Similarly, it may be determined that a value range of w2 is [1, 1.00000011], and a value range of w3 is [7.85046229E-18, 7.85046229E-16].
As verified by the inventor, in order to achieve a more accurate calculation result, in the conversion mathematical relationship corresponding to the sleep apnea hypopnea index mean, a value of w1 may be specifically 1.00000011, a value of w2 may be specifically 1.00000011, and a value of w3 may be specifically 7.85046229E-17.
For another example, the input indicator IN-01 is the hypopnea index x1, the input indicator IN-06 is the apnea index x2, and the input indicator IN-07 is the pressure parameter x3, the output indicator OUT-09 is the apnea index mean y2, and a conversion mathematical relationship corresponding to the apnea index mean is:
y
2
=f
2(x1, x2, x3)
where, a parameter of the function f2 is determined based on the original mapping matrix and element values of a column (i.e., column 11) where the apnea index mean is located in at least one target mapping matrix (the 1st to 8th target mapping matrices are taken as an example here). For example, a relationship between the apnea index mean and the hypopnea index, the apnea index and the pressure parameter is a weighted sum, and the corresponding conversion mathematical relationship may be specifically:
where, w1, w2 and w3 are determined based on the original mapping matrix and element values of a column where the apnea index mean is located in the at least one target mapping matrix. For example, in the above Table 12, element values in an eleventh column of a row where the input indicator IN-01 is located are respectively: 0.50000006, 0.500000056, 5.55500801E-08, 5.55500801E-08, 5.55500801E-08, 5.55500801E-08, 5.55500801E-08, 5.55500801E-08. Then, the value range of w1 may be [5.55500801E-08, 0.50000006]. Similarly, it may be determined that the value range of w2 is [0.50000006, 1.00000006], and the value range of w3 is [2.22044605E-17, 2.22044605E-15].
As verified by the inventor, in order to achieve a more accurate calculation result, in the conversion mathematical relationship corresponding to the apnea index mean, the value of w1 may be specifically 0.50000006. 0.500000056 or 5.55500801E-08, the value of w2 may be specifically 0.50000006 or 1.00000006, and the value of w3 may be specifically 2.22044605E-16.
For yet another example, the input indicator IN-01 is the hypopnea index x1, the input indicator IN-06 is the apnea index x2, and the input indicator IN-07 is the pressure parameter x3, the output indicator OUT-10 is the hypopnea index mean y3, and a conversion mathematical relationship corresponding to the hypopnea index mean is:
y
3
=f
3(x1,x2,x3)
where, a parameter of the function f3 is determined based on the original mapping matrix and element values of a column (i.e., column 12) where the hypopnea index mean is located in at least one target mapping matrix (the 1st to 8th target mapping matrices are taken as an example here). For example, a relationship between the hypopnea index mean and the hypopnea index, the apnea index and the pressure parameter is a weighted sum, and the corresponding conversion mathematical relationship may be specifically:
where, w1, w2 and w3 are determined based on the original mapping matrix and element values of a column where the hypopnea index mean is located in the at least one target mapping matrix. For example, in the above Table 12, element values in a twelfth column of a row where the input indicator IN-01 is located are respectively: 0.50000006, 0.500000056, 1.00000006, 1.00000006, 1.00000006, 1.00000006, 1.00000006, 1.00000006. Then, the value range of w1 may be [0.500000056, 1.00000006]. Similarly, it may be determined that the value range of w2 is [5.55500802E-08, 0.50000006], and the value range of w3 is [−7.85046229E-16, −7.85046229E-18].
As verified by the inventor, in order to achieve a more accurate calculation result, in the conversion mathematical relationship corresponding to the hypopnea index mean, the value of w1 may be specifically 0.50000006, 0.500000056 or 1.00000006, the value of w2 may be specifically 5.55500802E-08 or 0.50000006, and the value of w3 may be specifically −7.85046229E-17.
That is, the data processing module is configured to train and obtain value ranges of relational parameters w1, w2 and w3 in the following manner.
Sample medical data and sample business data are obtained. The sample medical data includes 9 input indicators, and the sample business data includes 10 output indicators. The input indicators include the hypopnea index x1, the apnea index x2 and the pressure parameter x3, the output indicators include the sleep apnea hypopnea index mean y1, the apnea index mean y2, and the hypopnea index mean y3.
An original mapping matrix R is constructed based on the obtained sample medical data and sample business data. An element R(i, j) in the original mapping matrix is used to represent an relationship between the input indicator i in the sample medical data and the output indicator j in the sample business data, i and j are positive integers, and i∈[1, 9], j∈[1, 10]. When there is a correlation between the input indicator i and the output indicator j, R (i, j)=1, and when it cannot be determined that whether there is the correlation between the input indicator i and the output indicator j, R (i, j)=0.
A first matrix P and a second matrix Q are obtained based on the original mapping matrix R. The first matrix represents a degree of influence of the input indicator on each output indicator, and the second matrix represents a degree of the output indicator being influenced by each input indicator.
The step of obtaining the first matrix P and the second matrix Q based on the original mapping matrix R includes:
The step of determining the value ranges of the relational parameters w1, w2 and w3 based on the original mapping matrix R and the element values of the column where the output indicator is located in the plurality of target mapping matrices Rk includes:
The above data conversion and model training processes may be completed by the data processing module of the data lake cluster.
The method for determining the parameter of the function f is exemplified in the above embodiments. Those skilled in the art can easily understand that in other embodiments of the present disclosure, depending on a main algorithm of the function f, its parameter may also be determined in other ways using the data in the above Table 12, which also falls within the scope of the present disclosure.
Referring to
The alarm terminal 1102 can be communicatively connected with the dispatch center terminal 1101 and is configured to send alarm information to the dispatch center terminal 1101. In an embodiment of the present disclosure, the alarm terminal 1102 is a device capable of collecting the user's voice, image, video, position and other information. For example, the alarm terminal 1102 may be a mobile electronic device, such as a smartphone, a smart watch, or a smart bracelet. The alarm terminal 1102 may also be an Internet of Things device in a community, park, or factory, such as a monitoring device or other information collection device. After the user triggers an alarm function of the alarm terminal 1102, the alarm terminal 1102 can establish the communication connection with the dispatch center terminal 1101, so as to collect the user's voice, image, video, position or other information and transmit it to the dispatch center terminal 1101.
The dispatch center terminal 1101 is configured to receive the alarm information and obtain a position of the subject to be treated, and dispatch, according to a preset dispatch algorithm, an ambulance vehicle (for example, and an ambulance resource) to the position of the subject to be treated to provide treatment. In an embodiment of the present disclosure, the dispatch center terminal 1101 may be deployed with a distributed agent system and a comprehensive display system (such as a large LCD splicing screen), so as to facilitate dispatching and outing of the ambulance vehicle, the deployment of the emergency medical personnel in the hospital, patient condition and treatment arrangements, etc. through the preset dispatch algorithm combined with manual coordination and management. In embodiments of the present disclosure, the preset dispatch algorithm may include, for example, establishing a regional road network model, and generating an optimal path according to an emergency plan and the regional road network model, and then dispatching the ambulance vehicle or the like based on the regional road network model and the optimal path.
In embodiments of the present disclosure, the dispatch center terminal 1101 may further be configured to: intelligently identify an emergency scene based on the received each piece of alarm information; when receiving new alarm information, determine whether to configure a new ambulance vehicle and a new ambulance resource based on the ambulance vehicle and the ambulance resource configured for the identified emergency scene if it is determined that the new alarm information is located in the identified emergency scene.
For example, the dispatch center terminal 1101 may intelligently identify the emergency scene through audio and video semantic analysis, big data processing and other technologies based on the alarm information generated in similar positions (such as within 1 kilometer, etc.) within a certain period of time (such as within 1 hour, etc.), and record it. When the new alarm information is received, it may be determined whether the new alarm information is located in the identified emergency scene according to a scene corresponding to the new alarm information. If it is determined that the new alarm information is located in the identified emergency scene, and the ambulance vehicle and the ambulance resource configured for the identified emergency scene can already meet a treatment plan corresponding to the new alarm information, there is no need to configure the new ambulance vehicle and ambulance resource. Otherwise, an additional ambulance vehicle and an additional ambulance resource will be dispatched to meet the treatment plan corresponding to the new alarm information.
In addition, the dispatch center terminal 1101 may also synchronize the alarm information to an emergency station in the community or factory area near an alarm caller or other institutions that can provide first aid services based on a position of the alarm caller and other information, thereby facilitating an on-duty volunteer or other rescuers to launch rescue operations as soon as possible, fully utilizing first aid resources, and avoiding delays in precious golden first aid time.
The rescuer terminal 1103 is communicatively connected with the dispatch center terminal 1101 and the medical business system, and is configured to receive dispatch information from the dispatch center terminal 1101. For example, the rescuer terminal 1103 may also receive medical data related to a treatment subject. As an example, medical record information of the treatment subject may be entered through the rescuer terminal 1103. In embodiments of the present disclosure, the rescuer terminal 1103 may be, for example, a dedicated communication terminal, or a smartphone installed with a relevant application, which can facilitate the rescuer to receive the dispatch information of the dispatch center terminal 1101, and also facilitate the rescuer to feed back information to the dispatch center terminal 1101.
In embodiments of the present disclosure, the dispatch center terminal 1101 may further be configured to dispatch an internal rescuer of the medical institution to provide remote assistance based on the medical data related to the subject to be treated. Specifically, as mentioned above, the medical institution may obtain the real-time medical data and the historical medical data of the subject to be treated from the data lake cluster by means of the medical business system in embodiments of the present disclosure. Therefore, the internal rescuer of the medical institution can use this to provide the remote assistance, such as a remote diagnosis, a remote device operation, etc. In addition, it can also facilitate the medical institution to carry out relevant preparations in advance, further reducing the waste of treatment time.
In addition, in the conventional emergency dispatch system, a treatment capacity and bed information of the medical institution cannot be known in time in the emergency vehicle dispatching and transportation process, which may cause a situation where it is found that the medical institution does not have the treatment capacity upon arriving at the medical institution, resulting in multiple transfers, which wastes the first aid resource and delays the precious golden first aid time. In embodiments of the present disclosure, in order to cope with the rapid real-time changes in the corresponding ambulance resources during the treatment and transportation process and avoid the occurrence of multiple transfers, the dispatch center terminal 1101 may further obtain first aid information (e.g., a bed condition, a treatment resource) of each hospital in real time, thereby facilitating the dispatch center terminal 1101 to guide the ambulance vehicle to transport the subject to be treated to the preferred medical institution; at the same time, the dispatch center terminal 1101 may also send the treatment capacity and the bed information of the medical institution to the rescuer terminal 1103, so that the rescuer can more accurately determine whether the transfer is needed, thereby reducing the impact of sudden changes in the bed condition.
The driver terminal 1104 is communicatively connected with the dispatch center terminal 1101 and is configured to guide a driver to drive the ambulance vehicle to the position of the subject to be treated. The driver terminal 1104 may be a vehicle-mounted terminal, such as a vehicle-mounted central control screen, a vehicle-mounted navigator, etc. Alternatively, the driver terminal 1104 may also be a smartphone or other communication terminal installed with a relevant application.
In some implementations, the driver terminal 1104 may display a current traveling process in real time. For example, when the ambulance vehicle arrives at the alarm position, a display state is “arrived at the alarm position”; when the ambulance vehicle is rushing to the treatment hospital, the display state is “on the way”; and when the ambulance vehicle arrives at the treatment hospital, the display state is “arrived at the hospital”, and the like.
Referring to
The information integration platform 1201 is configured to retrieve a personal health file and/or a knowledge base from a third-party business system 1202, and send them to the at least one of the management terminal 701, the dispatch center terminal 1101, the alarm terminal 1102, the rescuer terminal 1103, the driver terminal 1104 and the medical business system 103; and/or,
In some embodiments, the personal health file, such as historical medical information, medical history, an inspection report, etc., in the third-party business system 1202 is integrated through the information integration platform 1201, and sent to the at least one of the management terminal 701, the dispatch center terminal 1101, the alarm terminal 1102, the rescuer terminal 1103, the driver terminal 1104 and the medical business system 103. The user information (including the medical record information) stored in these terminals is integrated and compared with the personal health file in the information integration platform 1201 to facilitate a more comprehensive understanding of the patient's personal health condition. For example, key information such as a mental illness, an infectious disease, an examination record, etc. may also be extracted and displayed to the dispatch center terminal 1101, which facilitates a dispatcher to quickly make a prediction of the alarm situation.
In some implementations, the information integration platform 1201 is integrated with the knowledge base in the third-party business system 1202, and the knowledge base may be synchronized to the dispatch center terminal 1101, the rescuer terminal 1103, etc., to facilitate the standardized processing.
In some implementations, the dispatch center terminal 1101 can synchronize the dispatch information of the dispatcher during the dispatch process, such as a preliminary diagnosis, graphic guidance, an audio and video, etc., to the information integration platform 1201, and synchronize the treatment information to the information integration platform 1201 through the rescuer terminal 1103, etc.
In some implementations, the information integration platform 1201 may include a decision evaluation module, and the decision evaluation module evaluates the information of the subject to be treated based on the knowledge base. An effective evaluation is performed uniformly by the decision evaluation module. Based on an evaluation result, the relevant personnel can make targeted enhancements and update the knowledge base at the same time. For example, the decision evaluation module predicts a condition of the subject to be treated based on the knowledge base, or provides a standard treatment method based on the knowledge base.
In some embodiments, the information integration platform 1201 may further be configured to: intelligently identify an emergency scene based on the received each piece of alarm information; when receiving new alarm information, determine whether to configure a new ambulance vehicle and a new ambulance resource based on the ambulance vehicle and the ambulance resource configured for the identified emergency scene if it is determined that the new alarm information is located in the identified emergency scene.
It can be understood that at least some of the above functions may also be implemented by the dispatch center terminal 1101. For example, after the information integration platform 1201 completes the intelligent identification of the emergency scene, it sends scene information to the dispatch center terminal 1101, and the dispatch center terminal 1101 dispatches the ambulance vehicle and the ambulance resource based on specific conditions.
For example, the emergency scene is identified as a collective public event based on the received each piece of alarm information, and there may be a situation where a plurality of persons give alarms at a position of the collective public event. For example, the emergency scene may be identified based on the information provided by the person who give the alarm, or through environmental information such as voice and video. In the case that the plurality of persons give the alarms in the same area, the information integration platform 1201 can assign a certain number of ambulance vehicles and configure corresponding ambulance resources based on an analysis result. For example, when two people with minor injuries give alarms, it is considered that the assignment of one ambulance vehicle is enough, so as to save the ambulance resource.
For example, the dispatch center terminal 1101 may intelligently identify the emergency scene through audio and video semantic analysis, big data processing and other technologies based on the alarm information generated in similar positions (such as within 1 kilometer, etc.) within a certain period of time (such as within 1 hour, etc.), and record it. When the new alarm information is received, it may be determined whether the new alarm information is located in the identified emergency scene according to a scene corresponding to the new alarm information. If it is determined that the new alarm information is located in the identified emergency scene, and the ambulance vehicle and the ambulance resource configured for the identified emergency scene can already meet a treatment plan corresponding to the new alarm information, there is no need to configure the new ambulance vehicle and ambulance resource. Otherwise, an additional ambulance vehicle and an additional ambulance resource will be dispatched to meet the treatment plan corresponding to the new alarm information.
For example, the alarm terminal 1102, when making an alarm, uploads alarm information (the position, the audio and video), etc. to the information integration platform 1201. The dispatch center terminal 1101 uploads the dispatch information (the preliminary diagnosis, the vehicle assignment, the operation record, the audio and video and other information) to the information integration platform 1201. The dispatcher may mark an emergency level (such as very urgent, normal urgent, not urgent, etc.) at the dispatch center terminal 1101, and the emergency level is summarized to the dispatch information and uploaded to the information integration platform 1201. The information integration platform 1201 intelligently identifies the emergency scene based on the number of alarms generated in similar positions during the latest time, the audio and video semantic analysis, etc., synchronizes the emergency condition to the dispatch center terminal 1101, and at the same time, after a new alarm occurs, may intelligently determines, based on the newly summarized alarm information, whether the described scene and symptom are the same and whether the vehicle has been assigned. and conduct a targeted prompt. If the information integration platform 1201 identifies the emergency scene, it can summarize the existing alarm information and the dispatch information and display them in the 120 emergency command platform according to a geographic location in the dispatch information and a scene description in the audio and video semantic analysis, which facilitates timely emergency command and unified coordination.
In addition, the dispatch center terminal 1101 and/or the information integration platform 1201 may also synchronize the alarm information to an emergency station in the community or factory area near an alarm caller or other institutions that can provide first aid services based on a position of the alarm caller and other information, thereby facilitating an on-duty volunteer or other rescuers to launch rescue operations as soon as possible, fully utilizing first aid resources, and avoiding delays in precious golden first aid time.
In some embodiments, the rescuer terminal 1103 is further configured to record the treatment information, and the information integration platform 1201 is configured to draw the treatment information records as a timeline according to a chronological order and send it to the medical business system 103.
For example, the rescuer terminal 1103 collects an on-site video and identifies patient identity information, and uploads them to the information integration platform 1201 in a unified manner. A doctor can, by means of the rescuer terminal 1103, take real-time photos or videos of or manually input the physical, drug and other treatment operations carried out, for the upload to the information integration platform 1201 in real time, and the operation time is recorded upon uploading. The information integration platform 1201 forms a timeline of in-vehicle operations and summarize with location information for displaying to the medical business system. The information integration platform 1201 intelligently sorts patients who are about to arrive at the hospital based on treatment information, condition, distance, etc., so that emergency doctors determines a time to arrive at the hospital based on a time of treatment in the vehicle and a position of the vehicle, customizes a rescue plan, and coordinates hospital resources.
In some embodiments, the information integration platform 1201 is configured to receive hospital resource information sent from the medical business system 103 and send it to the dispatch center terminal 1101.
For example, through the information integration platform 1201, each hospital can, through the medical business system, timely update and synchronize the bed condition, the treatment capability, etc. of each hospital to the information integration platform 1201. The dispatcher can perform the selective dispatch according to an empty bed condition at the dispatch center terminal 1101. During the process of transporting the patient, the doctor in the vehicle can check a real-time bed condition of the heading-to hospital at the rescuer terminal 1103, which facilitates timely confirmation of the need to transfer to a hospital and reduces the impact caused by a sudden bed change.
It should be noted that although several modules or units of devices for executing actions are mentioned in the above detailed description, such division of modules or units is not mandatory. In fact, features and functions of two or more of the modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Alternatively, the features and functions of one module or unit described above may be further divided into multiple modules or units.
Various component embodiments of the present disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof.
Furthermore, there is further provided an emergency treatment method in embodiments of the present disclosure. The emergency treatment method may be applied to an emergency treatment system. Referring to
In the step S1301, medical data of a subject to be treated is collected by an information collection terminal deployed in an ambulance vehicle, and uploaded to a data lake cluster.
In the step S1302, data access, data processing, data storage and data distribution are performed for the medical data of the subject to be treated, by the data lake cluster.
In the step S1303, the medical data of the subject to be treated is obtained from the data lake cluster by a medical business system.
Specific details of each step in the above emergency treatment method have been described in detail in the corresponding emergency treatment system, which will not be described again here.
It should be understood that although the steps in the flowchart of the drawings are shown in an order as indicated by the arrows, those steps are not necessarily performed in the order as indicated by the arrows. The steps are not strictly limited to their current order, but may be performed in other orders, unless explicitly stated herein. Furthermore, at least a portion of the steps in the flowcharts of the drawings may include a plurality of sub-steps or stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution thereof is not necessarily performed sequentially, and may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In an embodiment of the present disclosure, there is further provided an electronic device, including: a processor; a memory configured to store instructions executable by the processor; and the processor is configured to execute the method described in any one of embodiments of the present disclosure.
As shown in
The input/output interface 1405 is connected with the following components: an input portion 1406 including a keyboard, a mouse, etc.; an output portion 1407 including such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storing portion 1408 including a hard disk, etc.; and a communication portion 1409 including a network interface card such as a local area network (LAN) card, a modem, etc. The communication portion 1409 performs communication processing via a network such as the Internet. A driver 1410 is also connected to the input/output interface 1405 as needed. A removable medium 1411, such as a disk, an optical disk, a magnetic disk, a semiconductor memory, etc., is mounted on the driver 1410 as needed, such that the computer programs as read from the removable medium 1411 may be mounted into the storing portion 1408 as needed.
In particular, according embodiments of the present disclosure, the process described above with reference to the flowchart may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product including a computer program carried on a computer-readable medium, and the computer program includes a program code for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 1409, and/or from the removable medium 1411. The computer program, when executed by the central processing unit 1401, performs various functions as defined in the device of the present disclosure.
In embodiments of the present disclosure, there is further provided a non-volatile computer-readable storage medium having a computer program stored thereon, which, when executed by a computer, causes the computer to execute any of the above methods.
It should be noted that the non-volatile computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination thereof. Examples of the computer-readable storage medium may include, but are not limited to: an electrical connection with at least one wire, a portable computer disk, a hard disk, a random-access memory, a read-only memory, an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storing device, a magnetic storing device, or any suitable combination thereof. In the present disclosure, the computer-readable storage medium may be any tangible medium that includes or stores a program, and the program may be adopted by or in combination with an instruction executing system, apparatus or device. In addition, in the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave that carries a computer-readable program code. The data signals as propagated may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium may also be any computer-readable medium that sends, propagates, or transmits the program as adopted by or in combination with the instruction executing system, apparatus or device. The program code included on the computer-readable medium may be transmitted via any suitable medium, including but not limited to wireless, wire, fiber optic cable, radio frequency, etc., or any suitable combination thereof.
Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the present disclosure disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present disclosure, which are in accordance with the general principles of the present disclosure and include common general knowledge or conventional technical means in the art that are not disclosed in the present disclosure. The specification and embodiments are illustrative, and the real scope and spirit of the present disclosure is defined by the appended claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 202110745539.X | Jul 2021 | CN | national |
The present application is the 371 application of PCT Application No. PCT/CN2022/103328, filed Jul. 1, 2022, which is based upon and claims the priority to the Chinese Patent Application NO. 202110745539. X, entitled “EMERGENCY TREATMENT SYSTEM, EMERGENCY TREATMENT METHOD, AND ELECTRONIC DEVICE”, filed on Jul. 1, 2021, the entire contents of which are hereby incorporated by reference.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/CN2022/103328 | 7/1/2022 | WO |
