Patent Application
20210038166
The present invention, in some embodiments thereof, relates to medicine and, more particularly, but not exclusively, to a method and system for predicting childhood obesity.
Over the past decades, the prevalence of childhood obesity has rapidly increased worldwide. A global analysis demonstrated that in 2016, 50 million girls and 74 million boys worldwide were obese, making it a global public health crisis. Obese children are very likely to have obesity persist into adulthood. Childhood obesity is associated with elevated blood pressure and lipids, and increased risk of diseases, such as asthma, type 2 diabetes, arthritis, and cardiovascular diseases at a later stage of life. Furthermore, childhood obesity can have a negative psycho-social effect.
Preventing excess weight gain in children is important for numerous reasons. Pediatric obesity is a multisystem disease that can greatly impact a child's physical and mental health. It is associated with a greater risk for premature mortality and earlier onset of chronic disorders such as hypertension, dyslipidemia, ischemic heart disease and type 2 diabetes, with insulin resistance identified in obese children as young as 5 years of age. Furthermore, there is currently an underestimation of obesity by parents and physicians and there is currently little guidance for health care professionals to identify infants at risk. Additionally, young age is a suitable time period for intervention, as it is associated with more beneficial long-term outcomes after lifestyle modifications.
According to an aspect of some embodiments of the present invention there is provided a method of predicting likelihood for childhood obesity. The method comprises: obtaining a plurality of parameters, wherein at least a few of the parameters characterize an infant or toddler subject; accessing a computer readable medium storing a machine learning procedure trained for predicting likelihoods for childhood obesity; feeding the procedure with the plurality of parameters; and receiving from the procedure an output indicative of a likelihood that the infant or toddler subject is expected to develop childhood obesity, wherein the output is related non-linearly to the parameters.
According to some embodiments of the invention the plurality of parameters comprises at least one parameter extracted from an electronic health record associated with the infant or toddler subject.
According to some embodiments of the invention the method comprises presenting to a user, by a user interface, a questionnaire and a set of questionnaire controls, receiving a set of response parameters entered by the user using the questionnaire controls, wherein the plurality of parameters comprises the response parameters.
According to some embodiments of the invention the plurality of parameters comprises at least one parameter extracted from a body liquid test applied to the infant or toddler subject.
According to some embodiments of the invention the plurality of parameters comprises at least one parameter characterizing a parent or a sibling of the infant or toddler subject.
According to some embodiments of the invention the at least one parameter characterizing the parent comprises a parameter extracted from a body liquid test applied to the parent or sibling.
According to some embodiments of the invention the plurality of parameters comprises at least one parameter extracted from a diagnosis previously recorded for the subject.
According to some embodiments of the invention the plurality of parameters comprises at least one parameter indicative of a pharmaceutical prescribed for the infant or toddler subject.
According to some embodiments of the invention the infant or toddler subject is less than two years of age.
According to some embodiments of the invention the infant or toddler subject is not obese. According to some embodiments of the invention the method wherein the infant or toddler subject has a normal weight. According to some embodiments of the invention the plurality of parameters comprises a weight-for-length score of the infant or toddler subject.
According to some embodiments of the invention the plurality of parameters comprise a weight of the infant or toddler subject at age of from about 4 to about 6 months, a weight of the infant or toddler subject at age of from about 12 to about 16 months, and a weight of the infant or toddler subject at age of from about 18 to about 22 months.
According to some embodiments of the invention the plurality of parameters comprises a parameter pertaining to a body-mass-index of a sibling of the infant or toddler subject.
According to some embodiments of the invention the plurality of parameters comprises a parameter pertaining to a body-mass-index of a father of the infant or toddler subject.
According to some embodiments of the invention the plurality of parameters comprises a result of a hemoglobin concentration test applied to the infant or toddler subject.
According to some embodiments of the invention the wherein the plurality of parameters comprises a result of a mean platelet volume test applied to the infant or toddler subject.
According to some embodiments of the invention the plurality of parameters comprises at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 100 or at least 200 or at least 300 or at least 400 or at least 500 or more of the parameters listed in Table 1.1.
According to some embodiments of the invention the plurality of parameters comprises at least 10 or at least 12 or at least 14 or at least 16 of the parameters that are listed at lines 1-40 more preferably lines 1-30 more preferably lines 1-20 of Table 1.1.
According to some embodiments of the invention the plurality of parameters comprises at least 20 or at least 22 or at least 24 or at least 26 or at least 28 or at least 30 or at least 32 or at least 34 or at least 36 of the parameters that are listed at lines 1-50 more preferably lines 1-45 more preferably lines 1-40 of Table 1.1.
According to some embodiments of the invention the plurality of parameters comprises least 50 or at least 60 or at least 70 or at least 80 or at least 90 of the parameters that are listed at lines 1-300 more preferably lines 1-200 more preferably lines 1-100 of
Table 1.1.
According to an aspect of some embodiments of the present invention there is provided a method of predicting likelihood for childhood obesity. The method comprises: obtaining a plurality of parameters characterizing at least one of a parent and a sibling of an unborn subject; accessing a computer readable medium storing a machine learning procedure trained for predicting likelihoods for childhood obesity; feeding the procedure with the plurality of parameters; and receiving from the procedure an output indicative of a likelihood that the unborn subject is expected to develop childhood obesity after birth, wherein the output is related non-linearly to the parameters.
According to some embodiments of the invention the plurality of parameters comprises at least one parameter extracted from an electronic health record associated with the at least one of the parent and the sibling.
According to some embodiments of the invention the method comprises presenting to a user, by a user interface, a questionnaire and a set of questionnaire controls, receiving a set of response parameters entered by the user using the questionnaire controls, wherein the plurality of parameters comprises the response parameters.
According to some embodiments of the invention the plurality of parameters comprises at least one parameter extracted from a body liquid test applied to the at least one of the parent and the sibling.
According to some embodiments of the invention the plurality of parameters comprises a parameter pertaining to a body-mass-index of the sibling.
According to some embodiments of the invention the plurality of parameters comprises a parameter pertaining to a body-mass-index of a father of the unborn subject.
According to some embodiments of the invention the plurality of parameters comprises at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 100 or at least 200 or at least 300 or at least 400 or at least 500 or at least 1,000 or at least 1,500 or more of the parameters listed in Table 1.2.
According to some embodiments of the invention the plurality of parameters comprises at least 10 or at least 12 or at least 14 or at least 16 of the parameters that are listed at lines 1-40 more preferably lines 1-30 more preferably lines 1-20 of Table 1.2.
According to some embodiments of the invention the plurality of parameters comprises at least 20 or at least 22 or at least 24 or at least 26 or at least 28 or at least 30 or at least 32 or at least 34 or at least 36 of the parameters that are listed at lines 1-50 more preferably lines 1-45 more preferably lines 1-40 of Table 1.2.
According to some embodiments of the invention the plurality of parameters comprises least 50 or at least 60 or at least 70 or at least 80 or at least 90 of the parameters that are listed at lines 1-300 more preferably lines 1-200 more preferably lines 1-100 of Table 1.2.
According to an aspect of some embodiments of the present invention there is provided a method of predicting likelihood for childhood obesity. The method comprises: presenting on a user interface a questionnaire and a set of questionnaire controls, and receiving from the user interface a set of response parameters entered using the questionnaire controls, wherein the set of response parameters characterizes an infant or toddler subject; accessing a computer readable medium storing a machine learning procedure trained for predicting likelihoods for childhood obesity; feeding the procedure with the set of parameters; and receiving from the procedure an output indicative of a likelihood that the infant or toddler subject is expected to develop childhood obesity, wherein the output is related non-linearly to the parameters.
According to some embodiments of the invention the plurality of parameters comprises at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or more of the parameters listed in Table 1.3.
According to some embodiments of the invention the plurality of parameters comprises at least 10 or at least 12 or at least 14 or at least 16 of the parameters that are listed at lines 1-40 more preferably lines 1-30 more preferably lines 1-20 of Table 1.3.
According to some embodiments of the invention the plurality of parameters comprises at least 20 or at least 22 or at least 24 or at least 26 of the parameters that are listed at lines 1-50 more preferably lines 1-40 more preferably lines 1-30 of Table 1.3.
According to an aspect of some embodiments of the present invention there is provided a method of predicting likelihood for childhood obesity. The method comprises: presenting on a user interface a questionnaire and a set of questionnaire controls, and receiving from the user interface a set of response parameters entered using the questionnaire controls, wherein the set of response parameters characterizes at least one of a parent and a sibling of an unborn subject; accessing a computer readable medium storing a machine learning procedure trained for predicting likelihoods for childhood obesity; feeding the procedure with the set of parameters; and receiving from the procedure an output indicative of a likelihood that the unborn subject is expected to develop childhood obesity after birth, wherein the output is related non-linearly to the parameters.
According to some embodiments of the invention the plurality of parameters comprises at least 5 or at least 10 or at least 15 or more of the parameters listed in Table 1.4.
According to some embodiments of the invention the plurality of parameters comprises at least 5 or at least 10 of the parameters that are listed at lines 1-15 of Table 1.4.
Unless otherwise defined, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the invention, exemplary methods and/or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.
Implementation of the method and/or system of embodiments of the invention can involve performing or completing selected tasks manually, automatically, or a combination thereof. Moreover, according to actual instrumentation and equipment of embodiments of the method and/or system of the invention, several selected tasks could be implemented by hardware, by software or by firmware or by a combination thereof using an operating system.
For example, hardware for performing selected tasks according to embodiments of the invention could be implemented as a chip or a circuit. As software, selected tasks according to embodiments of the invention could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system. In an exemplary embodiment of the invention, one or more tasks according to exemplary embodiments of method and/or system as described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes a volatile memory for storing instructions and/or data and/or a non-volatile storage, for example, a magnetic hard-disk and/or removable media, for storing instructions and/or data. Optionally, a network connection is provided as well. A display and/or a user input device such as a keyboard or mouse are optionally provided as well.
Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced.
In the drawings:
The present invention, in some embodiments thereof, relates to medicine and, more particularly, but not exclusively, to a method and system for predicting childhood obesity.
Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details of construction and the arrangement of the components and/or methods set forth in the following description and/or illustrated in the drawings and/or the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.
The processing operations of the present embodiments can be embodied in many forms. For example, they can be embodied in on a tangible medium such as a computer for performing the operations. They can be embodied on a computer readable medium, comprising computer readable instructions for carrying out the method operations. They can also be embodied in electronic device having digital computer capabilities arranged to run the computer program on the tangible medium or execute the instruction on a computer readable medium.
Computer programs implementing the method according to some embodiments of this invention can commonly be distributed to users on a distribution medium such as, but not limited to, CD-ROM, flash memory devices, flash drives, or, in some embodiments, drives accessible by means of network communication, over the internet (e.g., within a cloud environment), or over a cellular network. From the distribution medium, the computer programs can be copied to a hard disk or a similar intermediate storage medium. The computer programs can be run by loading the computer instructions either from their distribution medium or their intermediate storage medium into the execution memory of the computer, configuring the computer to act in accordance with the method of this invention. Computer programs implementing the method according to some embodiments of this invention can also be executed by one or more data processors that belong to a cloud computing environment. All these operations are well-known to those skilled in the art of computer systems. Data used and/or provided by the method of the present embodiments can be transmitted by means of network communication, over the internet, over a cellular network or over any type of network, suitable for data transmission.
The method according to preferred embodiments of the present invention can be embedded into healthcare systems and may allow identification and implementation of prevention strategies for children at high risk for obesity.
The method begins at 10 and continues to 11 at which a plurality of parameters characterizing is obtained. The inventors discovered that the likelihood for childhood obesity can be predicted both for infant or toddler subjects and for unborn subjects, e.g., during the pregnancy of a female carrying the unborn subject.
As used herein “infant” refers to an individual not more that 1 year of age, and “toddler” refers to an individual above 1 year of age and not more than 3 years of age”
Thus, in some embodiments of the present invention the method predicts likelihood that an infant or toddler subject is expected to develop childhood obesity, and in some embodiments of the present invention the method predicts unborn subject is expected to develop childhood obesity after birth. When the subject is an infant or toddler subject he or she is preferably of less than two years of age. The method of the present embodiments is typically used for estimating the likelihood that the subject is expected to develop childhood obesity at age greater than the toddler age, e.g., more than 4 years of age, for example, from about 5 to about 6 years of age.
When the subject is an infant or toddler subject, at least one of the parameters that are obtained at 11, more preferably more than one of these parameters, more preferably at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 100 or at least 200 or at least 300 or at least 400 or at least 500 or more of the parameters are extracted from an electronic health record associated with the subject. Parameters extracted from an electronic health record can include, but are not limited to, anthropometric parameters (e.g., height, weight, body mass index, weight-for-length score), blood pressure measurements, blood and urine laboratory tests, diagnoses recorded by physicians, and/or pharmaceuticals prescribed to the subject.
In some embodiments of the present invention at least one of the parameters that are obtained at 11, more preferably more than one of these parameters, more preferably at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 100 or at least 200 or at least 300 or at least 400 or at least 500 or more of the parameters are extracted from an electronic health record associated with a parent (mother and/or father) and/or a sibling (brother and or sister) of the subject. These parameters can include any of the aforementioned parameters associated with the subject, except that they describe the respective parent or sibling (e.g., anthropometric parameters, blood pressure measurements, blood and urine laboratory tests, diagnoses, pharmaceuticals).
When the subject is an unborn subject, there are typically no parameters that describe the subject itself, and so the parameters that are obtained at 11 are typically associated with a parent (mother and/or father) and/or a sibling (brother and or sister) of the subject, as further detailed hereinabove.
A list of parameters from which the parameters can be selected when the subject is an infant or toddler subject is provided in Table 1.1 of the Examples section that follows, and list of parameters from which the parameters can be selected when the subject is an unborn subject is provided in Table 1.2 of the Examples section that follows. In some embodiments of the present invention at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 100 or at least 200 or at least 300 or at least 400 or at least 500 are selected from the parameters listed in Table 1.1 (for an infant or toddler subject) or Table 1.2 (for an unborn subject). Preferably, but not necessarily, at least 10 or at least 12 or at least 14 or at least 16 of the parameters are selected from the parameters that are listed at lines 1-40 more preferably lines 1-30 more preferably lines 1-20 of Table 1.1 (for an infant or toddler subject) or Table 1.2 (for an unborn subject). In some embodiments, at least 20 or at least 22 or at least 24 or at least 26 or at least 28 or at least 30 or at least 32 or at least 34 or at least 36 of the parameters are selected from the parameters that are listed at lines 1-50 more preferably lines 1-45 more preferably lines 1-40 of Table 1.1 (for an infant or toddler subject) or Table 1.2 (for an unborn subject). In some embodiments, at least 50 or at least 60 or at least 70 or at least 80 or at least 90 of the parameters are selected from the parameters that are listed at lines 1-300 more preferably lines 1-200 more preferably lines 1-100 of Table 1.1 (for an infant or toddler subject) or Table 1.2 (for an unborn subject).
Also contemplated are embodiments in which the parameters are selected from a set of response parameters that are provided by a person on behalf of the subject (e.g., a parent, a sibling, etc.), by responding to a questionnaire presented to the person. These parameters can include anthropometric parameters (e.g., height, weight, body mass index, weight-for-length score), one or more parameters indicative of the age of the subject (if born), and one or more parameters indicative of the ethnicity of the subject. A list of parameters which can be provided by responding to the questionnaire is provided in Table 1.3 for the case in which the subject is an infant or toddler subject, and in Table 1.4 for the case in which the subject is an unborn subject.
In some embodiments of the present invention the parameters include only parameters extracted from one or more electronic health records, in some embodiments of the present invention the parameters include only response parameters that are provided on behalf of the subject, and in some embodiments of the present invention the parameters include both parameters extracted from electronic health record(s) and response parameters that are provided by the subject or on her behalf.
In some embodiments of the present invention the electronic health record(s) include a record that is associated with the subject, in some embodiments of the present invention parameters the electronic health record(s) include records that are associated with at least one of a parent and a sibling of the subject, and in some embodiments of the present invention the electronic health record(s) include at least one record that is associated with the subject, and at least one record that is associated with a parent and/or a sibling of the subject.
The number of parameters that are extracted from the electronic health record(s) associated is preferably at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 100 or at least 200 or at least 300 or at least 400 or at least 500 or more. The number of response parameters that are provided by the subject or on her behalf is preferably 100 or less, or 80 or less, or 70 or less. The advantage of this embodiment is that a relative small number of parameter allows the subject to manually respond to the questionnaire at a relatively short time.
When the parameters include both parameters extracted from electronic health record(s), and response parameters that are provided on behalf of the subject, the number of parameters that are extracted from the electronic health record(s) is optionally and preferably significantly larger (e.g., at least 2 or at least 3 or at least 4 or at least 5 or at least 6 or at least 7 or at least 8 or at least 9 or at least 10 times larger) than the number of response parameters that are provided on behalf of the subject.
In some embodiments of the present invention at least one of the parameters is extracted from a body liquid test applied to the infant or toddler subject. Representative examples of body liquid tests from which a parameter can extracted from a body liquid test applied to the infant or toddler subject according to some embodiments of the present invention include, without limitation, Albumin test, Alk. phosphatase test, Atypical lymph. %-dif test, Atypical lymph-dif test, Basophils percentage (Baso %) test, Basophils (Baso abs) test, Bilirubin total test, Bilirubin-direct test, Calcium test, Chloride test, Cholesterol test, C-reactive protein test, Creatinine test, Eos % test, Eos.abs test, Eosinophils abs-dif test, Eosinophils %-dif test, Ferritin test, Gamma glutamyl transferase (Ggt) test, Glucose test, Got (ast) test, Alanine aminotransferase (Gpt (alt)) test, hemoglobin concentration (Hb) test, Hematocrit (Hct) test, Hematocrit/hemoglobin (Hct/hgb) ratio test, Hyper % test, Hypochromic red cells (Hypo %) test, Iron test, Ldh test, Luc abs test, Luc % test, Lym % test, Lymp.abs test, Lymphocytes %-dif test, Lymphocytes abs-dif test, Macro % test, Mean cell hemoglobin (Mch) test, mean hemoglobin concentration (Mchc) test, mean corpuscular volume (Mcv) test, Micro % test, Micro %/hypo % test, Mono % test, Mono.abs test, Monocytes abs-dif test, Monocytes %-dif test, mean platelet volume (Mpv) test, Mpxi test, Neut % test, Neut.abs test, Neutrophils abs-dif test, Neutrophils %-dif test, Pct test, Pdw test, Phosphorus test, platelet count blood (Plt) test, Potassium test, Protein-total test, Rbc test, red cell distribution width (Rdw) test, Red blood cell distribution width presented as the coefficient of variation (Rdw-cv) test, Sodium test, Stabs %-dif test, Stabs abs-dif test, T4-free test, Transferrin test, Triglycerides test, Thyroid-stimulating hormone (Tsh) test, Urea test, Uric acid test, and white blood cells (Wbc) test.
In some embodiments of the present invention at least one of the parameters is extracted from a body liquid test applied to the mother of the infant or toddler subject during pregnancy of the mother with the infant or toddler subject. Representative examples of body liquid tests from which a parameter can extracted from a body liquid test applied to the mother according to some embodiments of the present invention include, without limitation, Albumin, Alk. phosphatase, Alpha fetoprotein tm, Amylase, Aptt-r, Aptt-sec, Baso %, Baso abs, Bilirubin indirect, Bilirubin total, Bilirubin-direct, Blood type, Calcium, Chloride, Cholesterol-hdl, Cholesterol, Cholesterol/hdl, Cholesterol-ldl calc, Ck-creat.kinase(cpk), Cmv igg, Control ptt, Creatinine, Dhea sulphate, Eos %, Eos.abs, Eosinophils abs-dif, Eosinophils %-dif, Esr, Estradiol (e-2), Ferritin, Fibrinogen calcu, Fibrinogen, Folic acid, Fsh, Ggt, Globulin, Glom.filtr.rate, Glucose (gtt) 0′, Glucose (gtt) 120′, Glucose (gtt) 180′, Glucose (gtt) 60′, Glucose 50 g, Glucose, Got (ast), Gpt (alt), Hb, Hba, Hba2, Hbf, Hct, Hct/hgb ratio, Hdw, Hemoglobin a, Hemoglobin alc %, Hepatitis bs ab, Hyper %, Hypo %, Iron, Ldh, Lh, Li, Luc abs, Luc %, Lym %, Lymp.abs, Lymphocytes %-dif, Lymphocytes abs-dif, Macro %, Magnesium, Mch, Mchc, Mcv, Micro %, Micro %/hypo %, Mono %, Mono.abs, Monocytes abs-dif, Monocytes %-dif, Mpv, Mpxi, Neut %, Neut.abs, Neutrophils abs-dif, Neutrophils %-dif, Non-hdl_cholesterol, Normoblast. %, Normoblast.abs, Pct, Pdw, Phosphorus, Plt, Potassium, Progesterone, Prolactin, Protein-total, Pt %, Pt-inr, Pt-sec, Rbc, Rdw, Rdw-cv, Rubella ab igg, Sodium, Stabs %-dif, Stabs abs-dif, T3-free, T4-free, Toxoplasma igg, Transferrin, Triglycerides, Tsh, Urea, Uric acid, Vitamin b12, Vitamin d (25-oh), and Wbc.
In some embodiments of the present invention at least one of the parameters is extracted from a test applied to the mother of the infant or toddler subject prior to the pregnancy of the mother with the infant or toddler subject. Representative examples such tests include, without limitation, 17-oh-progesterone, Albumin, Alk. phosphatase, Aly, Aly %, Amylase, Androstenedione, Anti cardiolipin igg, Anti cardiolipin igm, Antithrombin-iii, Aptt-r, Aptt-sec, Baso %, Baso abs, Bilirubin indirect, Bilirubin total, Bilirubin-direct, Blood type, BMI, Ca-125, Calcium, Chloride, Cholesterol-hdl, Cholesterol, Cholesterol/hdl, Cholesterol-ldl calc, Ck-creat.kinase(cpk), Cmv igg, Complement c3, Complement c4, Control ptt, Cortisol-blood, C-reactive protein, Creatinine, Dhea sulphate, Eos %, Eos.abs, Eosinophils %-dif, Esr, Estradiol (e-2), Ferritin, Fibrinogen calcu, Fibrinogen, Folic acid, Free androgen index, Fsh, Ggt, Globulin, Glom.filtr.rate, Glucose 50 g, Glucose, Got (ast), Gpt (alt), Hb, Hba2, Hbf, Hct, Hct/hgb ratio, Hdw, Hemoglobin a, Hemoglobin alc %, Hepatitis bs ab, Hyper %, Hypo %, Iga, Iron, Ldh, Lh, Lic, Lic %, Luc abs, Luc %, Lym %, Lymp.abs, Lymphocytes %-dif, Lymphocytes abs-dif, Macro %, Magnesium, Mch, Mchc, Mcv, Micro %, Micro %/hypo %, Mono %, Mono.abs, Monocytes abs-dif, Monocytes %-dif, Mpv, Mpxi, Neut %, Neut.abs, Neutrophils abs-dif, Neutrophils %-dif, Non-hdl_cholesterol, Normoblast. %, Normoblast.abs, Pct, Pdw, Phosphorus, Plt, Potassium, Progesterone, Prolactin, Protein c activity, Protein-total, Prot-s antigen (free, Pt %, Pt-inr, Pt-sec, Rbc, Rdw, Rdw-cv, Rubella ab igg, Shbg, Sodium, T3-free, T3-total, T4-free, Testosterone-total, Toxoplasma igg, Transferrin, Triglycerides, Tsh, Urea, Uric acid, Vitamin b12, Vitamin d (25-oh), Vldl, Wbc, and Weight.
In some embodiments of the present invention the plurality of parameters comprises a result of a blood glucose test applied to the mother of the subject.
In some embodiments of the present invention at least one of the parameters is extracted from a test applied to the father of the infant or toddler subject. Representative examples of such tests include, without limitation, Age at the birth of the subject, BMI count, BMI max, BMI mean, BMI median, BMI min, BMI standard deviation (std), Height count, Height max, Height mean, Height median, Height min, Height std, max Cholesterol-hdl, max Cholesterol, max Cholesterol/hdl, max Cholesterol-ldl calc, max Glucose, max Non-hdl_cholesterol, max Triglycerides, mean Cholesterol-hdl, mean Cholesterol, mean Cholesterol/hdl, mean Cholesterol-ldl calc, mean Glucose, mean Non-hdl_cholesterol, mean Triglycerides, median Cholesterol-hdl, median Cholesterol, median Cholesterol/hdl, median Cholesterol-ldl calc, median Glucose, median Non-hdl_cholesterol, median Triglycerides, min Cholesterol-hdl, min Cholesterol, min Cholesterol/hdl, min Cholesterol-ldl calc, min Glucose, min Non-hdl_cholesterol, min Triglycerides, std Cholesterol-hdl, std Cholesterol, std Cholesterol/hdl, std Cholesterol-ldl calc, std Glucose, std Non-hdl_cholesterol, std Triglycerides, Weight count, Weight max, Weight mean, Weight median, Weight min, and Weight std.
In some embodiments of the present invention one or more of the parameters is a result of a hemoglobin concentration test (Hb) applied to the subject.
In some embodiments of the present invention one or more of the parameters is a result of a mean platelet volume test (Mpv) applied to the subject.
In some embodiments of the present invention one or more of the parameters is a result of a Basophils percentage test (Baso %) applied to the subject.
In some embodiments of the present invention one or more of the parameters is a result of a red cell distribution width test (Rdw) applied to the subject.
In some embodiments of the present invention one or more of the parameters is a result of a platelet count blood test (plt) applied to the subject.
In some embodiments of the present invention the parameters comprise at least one parameter extracted from a clinical or hospital diagnosis previously recorded for the subject. Representative examples of clinical and hospital diagnoses which can be used as parameters according to some embodiments of the present invention include, without limitation, Abdominal pain, Abnormal loss of weight, Abnormal weight gain, Accident/injury; nos, Acquired deformities of other parts of limbs, Acute and unspecified inflammation of lacrimal passages, Acute bronchiolitis, Acute bronchitis, Acute conjunctivitis, Acute laryngitis, Acute laryngotracheitis, Acute lymphadenitis, Acute myringitis without mention of otitis media, Acute nasopharyngitis (common cold), Acute nonsuppurative otitis media, Acute pharyngitis, Acute suppurative otitis media, Acute tonsillitis, Acute upper respiratory infections of multiple or unsp.sites, Acute upper respiratory infections of unspecified site, Agranulocytosis, Allergic rhinitis, Allergy, unspecified, not elsewhere classified, Allergy/allergic react nos, Anal fissure, Anemia other/unspecified, Anorexia, Asthma, Asthma, unspecified, Atopic dermatitis/eczema, Benign neoplasm of skin, site unspecified, Blepharitis, Blisters with epidermal loss,burn 2nd.deg.unspecified site, Bronchopneumonia, organism unspecified, Candidiasis of mouth, Candidiasis of skin and nails, Candidiasis of unspecified site, Cellulitis and abscess of finger, Cellulitis and abscess of unspecified sites, Chronic rhinitis, Chronic serous otitis media, Colitis, enteritis, gastroenteritis presumed infectious origin, Congenital anomalies of lower limb, including pelvic girdle, Congenital dislocation of hip, Congenital musculoskeletal deformities of sternocleidomastoid, Constipation, Contact dermatitis and other eczema, Contact dermatitis and other eczema, unspecified cause, Contusion of unspecified site, Convulsions, Cough, Croup, Delivery in a completely normal case, Dermatitis due to food taken internally, Dermatophytosis of the body, Diaper or napkin rash, Diarrhea, Diseases and other conditions of the tongue, Disorders relating to other preterm infants, Dyspnea and respiratory abnormalities, Enlargement of lymph nodes, Enteritis due to specified virus, Enterobiasis, Esophagitis, Feeding difficulties and mismanagement, Fever, Gastrointestinal hemorrhage, Hand, foot, and mouth disease, Hearing complaints, Hearing loss, Hemangioma of unspecified site, Herpangina, Hip symptoms/complaints, Hydrocele, Hydronephrosis, Hypermetropia, Hypertrophy of tonsils and adenoids, Impetigo, Infectious colitis, enteritis, and gastroenteritis, Infectious diarrhea, Infectious mononucleosis, Infective otitis externa, Influenza, Inguinal hernia, without mention of obstruction or gangrene, Injuries, Insect bite, Insect bite, nonvenomous face, neck, scalp without infection, Intestinal malabsorption, Iron deficiency anemia, unspecified, Irritable infant, Jaundice, unspecified, not of newborn, Laceration/cut, Lack of coordination, Lack of expected normal physiological development, Late effect of injury to cranial nerve, Laxity of ligament, Nausea and vomiting, Nervousness, Nonsuppurative otitis media, not specified as acute or chronic, Open wound of face, without mention of complication, Oral aphthae, Otalgia, Other and unspec.noninfectious gastroenteritis and colitis, Other and unspecified chronic nonsuppurative otitis media, Other and unspecified injury to unspecified site, Other atopic dermatitis and related conditions, Other diseases of conjunctiva due to viruses and chlamydiae, Other diseases of nasal cavity and sinuses, Other serum reaction, not elsewhere classified, Other specified disease of white blood cells, Other specified erythematous conditions, Other specified viral exanthemata, Other speech disturbance, Other symptoms involving digestive system, Other viral diseases; nos, Otorrhea, Pneumonia, Pneumonia, organism unspecified, Posttraumatic wound infection not elsewhere classified, Premat/immature liveborn infant, Rash and other nonspecific skin eruption, Seborrhea, Seborrheic dermatitis, unspecified, Serous otitis media;glue, Sleep disturbances, Sneezing/nasal congestion, Stenosis and insufficiency of lacrimal passages, Stomatitis, Strabismus and other disorders of binocular eye movements, Stridor, Teething syndrome, Tongue tie, Torticollis, unspecified, U.r.i. (head cold), Umbilical hernia without mention of obstruction or gangrene, Undescended testicle, Unsp.adv.effect of drug,medicinal/biological substance n.e.s., Unsp.viral infect.in conditions classif.elsewhere, unsp.site, Unspecified fetal and neonatal jaundice, Unspecified otitis media, Urinary tract infection, site not specified, Urticaria, Varicella without mention of complication, Viral exanthem, unspecified, Viral pneumonia, Volume depletion disorder, Vomiting (excl.preg. w06), and Wheezing baby syndrome.
In some embodiments of the present invention the parameters comprise at least one parameter indicative of a pharmaceutical prescribed for the subject. Representative examples of prescribed pharmaceuticals which can be used as parameters according to some embodiments of the present invention include, without limitation, Aciclovir, Ahiston drop cd, Amoxicillin, Azithromycin, Bethamethasone, Budesonide, Cefaclor, Cefalexin, Ceftriaxone, Cefuroxime, Co-amoxiclav cd, Co-trimoxazole cd, Desloratadine, Dimethindene, Erythromycin, Fluticasone, Ipratropium bromide, Ketotifen, Loratadine, Mebendazole, Metronidazole, Montelukast, Phenoxymethylpenicillin, Prednisolone, Prothiazine/promethazine expectorant cd, Ranitidine, Salbutamol, and Terbutaline.
In some embodiments of the present invention the parameters comprise at least one parameter indicative of a count of Salbutamol prescriptions provided for the infant or toddler subject.
In some embodiments of the present invention the parameters comprise at least one parameter indicative of a count of Bethamethasone prescriptions provided for the infant or toddler subject.
In some embodiments of the present invention the parameters comprise at least one parameter indicative of a count of Budesonide prescriptions provided for the infant or toddler subject.
In some embodiments of the present invention the parameters comprise at least one parameter indicative of a pharmaceutical prescribed for the mother of the subject. Representative examples of prescribed pharmaceuticals which can be used as parameters according to some embodiments of the present invention include, without limitation, Aciclovir, Amoxicillin, Anti-d (rh) immunoglobulin, Aspirin, Bethamethasone, Budesonide, Cabergoline, Carbamazepine, Cefalexin, Cefuroxime, Cetirizine, Choriogonadotropin alfa, Chorionic gonadotrophin, Ciprofloxacin, Citalopram, Clarithromycin, Clomifene, Clonazepam, Co-amoxiclav cd, Colchicine, Desloratadine, Desogestrel and ethinylestradiol, Desogestrel, Dexamethasone, Doxycycline, Drospirenone and ethinylestradiol, Dydrogesterone, Enoxaparin, Escitalopram, Estradiol, Famotidine, Fexofenadine, Fluconazole, Fluoxetine, Fluticasone, Follitropin alfa, Follitropin beta, Gestodene and ethinylestradiol, Human menopausal gonadotrophin, Ipratropium bromide, Lamotrigine, Lansoprazole, Levothyroxine sodium, Loratadine, Mebendazole, Medroxyprogesterone, Methylphenidate, Metronidazole, Nitrofurantoin, Norethisterone, Norgestimate and ethinylestradiol, Ofloxacin, Omeprazole, Paroxetine, Phenoxymethylpenicillin, Prednisone, Progesterone, Progyluton cd, Roxithromycin, Salbutamol, Seretide cd, Sertraline, Simvastatin, Symbicort/duoresp, and Triptorelin.
In some embodiments of the present invention the parameters comprise at least one parameter indicative of a pharmaceutical prescribed for the father of the subject. Representative examples of prescribed pharmaceuticals which can be used as parameters according to some embodiments of the present invention include, without limitation, Amlodipine, Atenolol, Atorvastatin, Bezafibrate, Bisoprolol, Cholesterol-hdl, Cholesterol, Cholesterol/hdl, Cholesterol-ldl calc, Enalapril, Glucose, Insulin glargine, Metformin and sitagliptin cd, Metformin, Nifedipine, Nifedipine-cd, Non-hdl_cholesterol, Pravastatin, Propranolol, Ramipril, Ramipril-hydrochlorothiazide cd, Rosuvastatin, Simvastatin, and Triglycerides.
In some embodiments of the present invention the parameters comprise at least one parameter extracted from a clinical or hospital diagnosis previously recorded for the father of subject. Representative examples of clinical and hospital diagnoses which can be used as parameters according to some embodiments of the present invention include, without limitation, Diabetes mellitus, unspecified Obesity, Obesity (BMI>30), other and unspecified hyperlipidemia, Essential hypertension, Morbid obesity, unspecified essential hypertension, Overweight (BMI<30), other abnormal glucose, Lipid metabolism disorder, Impaired fasting glucose, Disorders of lipoid metabolism, Diabetes mellitus without mention of complication, and Adult-onset type diabetes mellitus without complication.
Referring again to
As used herein the term “machine learning” refers to a procedure embodied as a computer program configured to induce patterns, regularities, or rules from previously collected data to develop an appropriate response to future data, or describe the data in some meaningful way.
Representative examples of machine learning procedures suitable for the present embodiments, include, without limitation, clustering, association rule algorithms, feature evaluation algorithms, subset selection algorithms, support vector machines, classification rules, cost-sensitive classifiers, vote algorithms, stacking algorithms, Bayesian networks, decision trees, neural networks, instance-based algorithms, linear modeling algorithms, k-nearest neighbors (KNN) analysis, ensemble learning algorithms, probabilistic models, graphical models, logistic regression methods (including multinomial logistic regression methods), gradient ascent methods, singular value decomposition methods and principle component analysis.
Following is an overview of some machine learning procedures suitable for the present embodiments.
Support vector machines are algorithms that are based on statistical learning theory. A support vector machine (SVM) according to some embodiments of the present invention can be used for classification purposes and/or for numeric prediction. A support vector machine for classification is referred to herein as “support vector classifier,” support vector machine for numeric prediction is referred to herein as “support vector regression”.
An SVM is typically characterized by a kernel function, the selection of which determines whether the resulting SVM provides classification, regression or other functions. Through application of the kernel function, the SVM maps input vectors into high dimensional feature space, in which a decision hyper-surface (also known as a separator) can be constructed to provide classification, regression or other decision functions. In the simplest case, the surface is a hyper-plane (also known as linear separator), but more complex separators are also contemplated and can be applied using kernel functions. The data points that define the hyper-surface are referred to as support vectors.
The support vector classifier selects a separator where the distance of the separator from the closest data points is as large as possible, thereby separating feature vector points associated with objects in a given class from feature vector points associated with objects outside the class. For support vector regression, a high-dimensional tube with a radius of acceptable error is constructed which minimizes the error of the data set while also maximizing the flatness of the associated curve or function. In other words, the tube is an envelope around the fit curve, defined by a collection of data points nearest the curve or surface.
An advantage of a support vector machine is that once the support vectors have been identified, the remaining observations can be removed from the calculations, thus greatly reducing the computational complexity of the problem. An SVM typically operates in two phases: a training phase and a testing phase. During the training phase, a set of support vectors is generated for use in executing the decision rule. During the testing phase, decisions are made using the decision rule. A support vector algorithm is a method for training an SVM. By execution of the algorithm, a training set of parameters is generated, including the support vectors that characterize the SVM. A representative example of a support vector algorithm suitable for the present embodiments includes, without limitation, sequential minimal optimization.
In KNN analysis, the affinity or closeness of objects is determined. The affinity is also known as distance in a feature space between objects. Based on the determined distances, the objects are clustered and an outlier is detected. Thus, the KNN analysis is a technique to find distance-based outliers based on the distance of an object from its kth-nearest neighbors in the feature space. Specifically, each object is ranked on the basis of its distance to its kth-nearest neighbors. The farthest away object is declared the outlier. In some cases the farthest objects are declared outliers. That is, an object is an outlier with respect to parameters, such as, a k number of neighbors and a specified distance, if no more than k objects are at the specified distance or less from the object. The KNN analysis is a classification technique that uses supervised learning. An item is presented and compared to a training set with two or more classes. The item is assigned to the class that is most common amongst its k-nearest neighbors. That is, compute the distance to all the items in the training set to find the k nearest, and extract the majority class from the k and assign to item.
Association rule algorithm is a technique for extracting meaningful association patterns among features.
The term “association”, in the context of machine learning, refers to any interrelation among features, not just ones that predict a particular class or numeric value. Association includes, but it is not limited to, finding association rules, finding patterns, performing feature evaluation, performing feature subset selection, developing predictive models, and understanding interactions between features.
The term “association rules” refers to elements that co-occur frequently within the datasets. It includes, but is not limited to association patterns, discriminative patterns, frequent patterns, closed patterns, and colossal patterns.
A usual primary step of association rule algorithm is to find a set of items or features that are most frequent among all the observations. Once the list is obtained, rules can be extracted from them.
The aforementioned self-organizing map is an unsupervised learning technique often used for visualization and analysis of high-dimensional data. Typical applications are focused on the visualization of the central dependencies within the data on the map. The map generated by the algorithm can be used to speed up the identification of association rules by other algorithms. The algorithm typically includes a grid of processing units, referred to as “neurons”. Each neuron is associated with a feature vector referred to as observation. The map attempts to represent all the available observations with optimal accuracy using a restricted set of models. At the same time the models become ordered on the grid so that similar models are close to each other and dissimilar models far from each other. This procedure enables the identification as well as the visualization of dependencies or associations between the features in the data.
Feature evaluation algorithms are directed to the ranking of features or to the ranking followed by the selection of features based on their impact.
Information gain is one of the machine learning methods suitable for feature evaluation. The definition of information gain requires the definition of entropy, which is a measure of impurity in a collection of training instances. The reduction in entropy of the target feature that occurs by knowing the values of a certain feature is called information gain. Information gain may be used as a parameter to determine the effectiveness of a feature in explaining the response to the treatment. Symmetrical uncertainty is an algorithm that can be used by a feature selection algorithm, according to some embodiments of the present invention. Symmetrical uncertainty compensates for information gain's bias towards features with more values by normalizing features to a [0,1] range.
Subset selection algorithms rely on a combination of an evaluation algorithm and a search algorithm. Similarly to feature evaluation algorithms, subset selection algorithms rank subsets of features. Unlike feature evaluation algorithms, however, a subset selection algorithm suitable for the present embodiments aims at selecting the subset of features with the highest impact on predicting likelihood for childhood obesity, while accounting for the degree of redundancy between the features included in the subset. The benefits from feature subset selection include facilitating data visualization and understanding, reducing measurement and storage requirements, reducing training and utilization times, and eliminating distracting features to improve classification.
Two basic approaches to subset selection algorithms are the process of adding features to a working subset (forward selection) and deleting from the current subset of features (backward elimination). In machine learning, forward selection is done differently than the statistical procedure with the same name. The feature to be added to the current subset in machine learning is found by evaluating the performance of the current subset augmented by one new feature using cross-validation. In forward selection, subsets are built up by adding each remaining feature in turn to the current subset while evaluating the expected performance of each new subset using cross-validation. The feature that leads to the best performance when added to the current subset is retained and the process continues. The search ends when none of the remaining available features improves the predictive ability of the current subset. This process finds a local optimum set of features.
Backward elimination is implemented in a similar fashion. With backward elimination, the search ends when further reduction in the feature set does not improve the predictive ability of the subset. The present embodiments contemplate search algorithms that search forward, backward or in both directions. Representative examples of search algorithms suitable for the present embodiments include, without limitation, exhaustive search, greedy hill-climbing, random perturbations of subsets, wrapper algorithms, probabilistic race search, schemata search, rank race search, and Bayesian classifier.
A decision tree is a decision support algorithm that forms a logical pathway of steps involved in considering the input to make a decision.
The term “decision tree” refers to any type of tree-based learning algorithms, including, but not limited to, model trees, classification trees, and regression trees.
A decision tree can be used to classify the datasets or their relation hierarchically. The decision tree has tree structure that includes branch nodes and leaf nodes. Each branch node specifies an attribute (splitting attribute) and a test (splitting test) to be carried out on the value of the splitting attribute, and branches out to other nodes for all possible outcomes of the splitting test. The branch node that is the root of the decision tree is called the root node. Each leaf node can represent a classification (e.g., whether a particular parameter influences on the likelihood for childhood obesity) or a value (e.g., the predicted likelihood for childhood obesity). The leaf nodes can also contain additional information about the represented classification such as a confidence score that measures a confidence level in the represented classification (i.e., the accuracy of the prediction).
Regression techniques which may be used in accordance with some embodiments the present invention include, but are not limited to linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression ordinal Probit-Regression, Poisson Regression, negative binomial Regression, multinomial logistic Regression (MLR) and truncated regression.
A logistic regression or logit regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (a dependent variable that can take on a limited number of values, whose magnitudes are not meaningful but whose ordering of magnitudes may or may not be meaningful) based on one or more predictor variables. Logistic regression may also predict the probability of occurrence for each data point. Logistic regressions also include a multinomial variant. The multinomial logistic regression model is a regression model which generalizes logistic regression by allowing more than two discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.). For binary-valued variables, a cutoff between the 0 and 1 associations is typically determined using the Yuden Index.
A Bayesian network is a model that represents variables and conditional interdependencies between variables. In a Bayesian network variables are represented as nodes, and nodes may be connected to one another by one or more links. A link indicates a relationship between two nodes. Nodes typically have corresponding conditional probability tables that are used to determine the probability of a state of a node given the state of other nodes to which the node is connected. In some embodiments, a Bayes optimal classifier algorithm is employed to apply the maximum a posteriori hypothesis to a new record in order to predict the probability of its classification, as well as to calculate the probabilities from each of the other hypotheses obtained from a training set and to use these probabilities as weighting factors for future predictions of the likelihood for childhood obesity. An algorithm suitable for a search for the best Bayesian network, includes, without limitation, global score metric-based algorithm. In an alternative approach to building the network, Markov blanket can be employed. The Markov blanket isolates a node from being affected by any node outside its boundary, which is composed of the node's parents, its children, and the parents of its children.
Instance-based techniques generate a new model for each instance, instead of basing predictions on trees or networks generated (once) from a training set.
The term “instance”, in the context of machine learning, refers to an example from a dataset.
Instance-based techniques typically store the entire dataset in memory and build a model from a set of records similar to those being tested. This similarity can be evaluated, for example, through nearest-neighbor or locally weighted methods, e.g., using Euclidian distances. Once a set of records is selected, the final model may be built using several different techniques, such as the naive Bayes.
Neural networks are a class of algorithms based on a concept of inter-connected “neurons.” In a typical neural network, neurons contain data values, each of which affects the value of a connected neuron according to connections with predefined strengths, and whether the sum of connections to each particular neuron meets a predefined threshold. By determining proper connection strengths and threshold values (a process also referred to as training), a neural network can achieve efficient recognition of images and characters. Oftentimes, these neurons are grouped into layers in order to make connections between groups more obvious and to each computation of values. Each layer of the network may have differing numbers of neurons, and these may or may not be related to particular qualities of the input data.
In one implementation, called a fully-connected neural network, each of the neurons in a particular layer is connected to and provides input value to those in the next layer. These input values are then summed and this sum compared to a bias, or threshold. If the value exceeds the threshold for a particular neuron, that neuron then holds a positive value which can be used as input to neurons in the next layer of neurons. This computation continues through the various layers of the neural network, until it reaches a final layer. At this point, the output of the neural network routine can be read from the values in the final layer. Unlike fully-connected neural networks, convolutional neural networks operate by associating an array of values with each neuron, rather than a single value. The transformation of a neuron value for the subsequent layer is generalized from multiplication to convolution.
The machine learning procedure used according to some embodiments of the present invention is a trained machine learning procedure, which provides output that is related non-linearly to the parameters with which it is fed.
A machine learning procedure can be trained according to some embodiments of the present invention by feeding a machine learning training program with parameters that characterizes each of a cohort of subjects that has been diagnosed as either having or not having childhood obesity at obesity at age greater than the toddler age. Once the data are fed, the machine learning training program generates a trained machine learning procedure which can then be used without the need to re-train it.
For example, when it is desired to employ decision trees, machine learning training program learns the structure of each tree in a plurality of decision trees (e.g., how many nodes there are in each tree, and how these are connected to one another), and also selects the decision rules for split nodes of each tree. At least a portion of the decision rules relate to one or more of the parameters that characterize the subject. A simple decision rule may be a threshold for the value of a particular parameter, but more complex rules, relating to more than one parameter are also contemplated. The machine learning training program also accumulates data at the leaves of the trees. The structures of the trees, the decision rules for the split nodes, and the data at the leaves are all selected by the machine learning training program, automatically and typically without user intervention, such that the parameters at the root of the trees provide the likelihood for childhood obesity at the leaves of the trees. The final result of the machine learning training program in this case is a set of trees, where the structures, the decision rules for split nodes, and leaf data for each trees are defined by the machine learning training program.
The method proceeds to 13 at which the trained machine learning procedure is fed with the parameters, and to 14 at which an output indicative of the likelihood that the subject is expected to develop childhood obesity is received from the procedure. Preferably, the procedure provides the likelihood that the subject is expected to develop childhood obesity at an age greater than the toddler are, as further detailed hereinabove. In some embodiments of the present invention the method proceeds to 15 at which a report predating to the likelihood is generated. The report can be displayed on a display device or transmitted to a computer readable medium.
The method ends at 16.
The prediction of likelihood for childhood obesity can be executed according to some embodiments of the present invention by a server-client configuration, as will now be explained with reference to
GUI 42 and processor 32 can be integrated together within the same housing or they can be separate units communicating with each other. GUI 42 can optionally and preferably be part of a system including a dedicated CPU and I/O circuits (not shown) to allow GUI 42 to communicate with processor 32. Processor 32 issues to GUI 42 graphical and textual output generated by CPU 36. Processor 32 also receives from GUI 42 signals pertaining to control commands generated by GUI 42 in response to user input. GUI 42 can be of any type known in the art, such as, but not limited to, a keyboard and a display, a touch screen, and the like. In preferred embodiments, GUI 42 is a GUI of a mobile device such as a smartphone, a tablet, a smartwatch and the like. When GUI 42 is a GUI of a mobile device, the CPU circuit of the mobile device can serve as processor 32 and can execute the method optionally and preferably by executing code instructions.
Client 30 and server 50 computers can further comprise one or more computer-readable storage media 44, 64, respectively. Media 44 and 64 are preferably non-transitory storage media storing computer code instructions for executing the method of the present embodiments, and processors 32 and 52 execute these code instructions. The code instructions can be run by loading the respective code instructions into the respective execution memories 38 and 58 of the respective processors 32 and 52. Storage media 64 preferably also store one or more databases including a database of psychologically annotated olfactory perception signatures as further detailed hereinabove.
In operation, processor 32 of client computer 30 displays on GUI 42 a questionnaire and a set of questionnaire controls, such as, but not limited to, a slider, a dropdown menu, a combo box, a text box and the like. A representative example of a displayed questionnaire 60 and a set of controls 62 is shown in
Processor 32 receives the response parameters from GUI 42 and typically transmits these parameters to server computer 50 over network 40. Media 64 can store a machine learning procedure trained for predicting likelihoods for childhood obesity. Server computer 50 can access media 64, feed the stored procedure with the parameters received from client computer 30, and receive from the procedure an output indicative of the likelihood that the subject that is characterized by the parameters is expected to develop childhood obesity. Server computer 50 can also transmit to client computer 30 the obtained likelihood, and client computer 30 can display this information on GUI 42.
As used herein the term “about” refers to ±10%.
The word “exemplary” is used herein to mean “serving as an example, instance or illustration.” Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments and/or to exclude the incorporation of features from other embodiments.
The word “optionally” is used herein to mean “is provided in some embodiments and not provided in other embodiments.” Any particular embodiment of the invention may include a plurality of “optional” features unless such features conflict.
The terms “comprises”, “comprising”, “includes”, “including”, “having” and their conjugates mean “including but not limited to”.
The term “consisting of” means “including and limited to”.
The term “consisting essentially of” means that the composition, method or structure may include additional ingredients, steps and/or parts, but only if the additional ingredients, steps and/or parts do not materially alter the basic and novel characteristics of the claimed composition, method or structure.
As used herein, the singular form “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “a compound” or “at least one compound” may include a plurality of compounds, including mixtures thereof.
Throughout this application, various embodiments of this invention may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.
Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases “ranging/ranges between” a first indicate number and a second indicate number and “ranging/ranges from” a first indicate number “to” a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals therebetween.
As used herein the term “method” refers to manners, means, techniques and procedures for accomplishing a given task including, but not limited to, those manners, means, techniques and procedures either known to, or readily developed from known manners, means, techniques and procedures by practitioners of the chemical, pharmacological, biological, biochemical and medical arts.
As used herein, the term “treating” includes abrogating, substantially inhibiting, slowing or reversing the progression of a condition, substantially ameliorating clinical or aesthetical symptoms of a condition or substantially preventing the appearance of clinical or aesthetical symptoms of a condition.
It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.
Various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below find experimental support in the following examples.
Reference is now made to the following examples, which together with the above descriptions illustrate some embodiments of the invention in a non limiting fashion.
Table 1.1 presents a list of 945 parameters from which parameters for feeing the machine learning procedure can be selected when the subject is an infant or toddler subject. The list is sorted according the significance of the respective feature for predicting the likelihood for childhood obesity, in descending order, so that from the standpoint of prediction accuracy it is more preferred to select a parameter that is listed higher in Table 1.1, than a parameter that is listed lower in Table 1.1. For example, when N parameters are used, it is preferred to select those parameters from lines 1 through M of Table 1.1, where N≤M≤945.
Table 1.2 presents a list of 620 parameters from which parameters for feeing the machine learning procedure can be selected when the subject is when the subject is an unborn subject. The list is sorted according the significance of the respective feature for predicting the likelihood for childhood obesity, in descending order, so that from the standpoint of prediction accuracy it is more preferred to select a parameter that is listed higher in Table 1.2, than a parameter that is listed lower in Table 1.2. For example, when N parameters are used, it is preferred to select those parameters from lines 1 through M of Table 1.2, where N≤M≤620.
Table 1.3 presents a list of 66 response parameters from which parameter to be included in questionnaire can be selected when the subject is an infant or toddler subject. The questionnaire can presented to a person on behalf of the subject, and can provide response parameters for feeing the machine learning procedure. The list is sorted according the significance of the respective feature for predicting the likelihood for childhood obesity, in descending order, so that from the standpoint of prediction accuracy it is more preferred to select a parameter that is listed higher in Table 1.3, than a parameter that is listed lower in Table 1.3. For example, when N parameters are used, it is preferred to select those parameters from lines 1 through M of Table 1.3, where N≤M≤66.
Table 1.4 presents a list of 21 response parameters from which parameter to be included in questionnaire can be selected when the subject is an unborn subject. The questionnaire can presented to a person on behalf of the subject, and can provide response parameters for feeing the machine learning procedure. The list is sorted according the significance of the respective feature for predicting the likelihood for childhood obesity, in descending order, so that from the standpoint of prediction accuracy it is more preferred to select a parameter that is listed higher in Table 1.4, than a parameter that is listed lower in Table 1.4. For example, when N parameters are used, it is preferred to select those parameters from lines 1 through M of Table 1.4, where N≤M≤21.
This Example describes analysis of data collected over a decade from Israel's largest healthcare provider, to assess risk factors for pediatric obesity and to develop a model for assessing children's obesity risk in order to inform and target interventions. The inventors analyzed nationwide electronic health records of children from 2006 to 2018 for whom sequential anthropometric data were available. Obesity was defined as body mass index (BMI)≥95th percentile for age and gender. Data of children and their families included anthropometric measurements, drug prescriptions, medical diagnoses, demographic data and laboratory tests.
Analysis of BMI trajectories among 382,132 adolescents revealed that among obese adolescents, the largest annual increase in BMI percentile occurs at 2-5 years of age. Therefore, the inventors devised a computational model based on data of 136,196 children from birth up to 2 years of age for predicting obesity at 5-6 years of age and from birth and up to 2 years of age. Most (51%) obese children in our cohort had a normal weight at infancy. As will be shown below, the model predicted obesity with an area under the receiver operating characteristic curve (auROC) and 95% CI of 0.803 [0.796−0.812]. Discrimination results on different subpopulations demonstrated its robustness across a clinically heterogeneous pediatric population. The most influential features included anthropometric measurements of the child and the family. Other impactful features included ethnicity and maternal pregnancy glucose measurements. A model based solely on features that are available pre-birth had similar performance to a model based on the child's last available weight and length measurements.
Extracted features included maternal, paternal and siblings' data.
All EHR data available were binned into time periods and statistical measures (e.g., median, max, slope) were taken as features for each period. Pharmaceutical prescriptions and clinical diagnoses were categorized by ATC codes (Anon n.d.) and ICD9 diagnosis codes, respectively, and counts in different time periods were taken as features. Weight, height, Weight-for-Length (WFL) and BMI data were converted to reference z-scores provided by the Center for Disease Control and Prevention (CDC) (Barlow and Expert Committee 2007). Valid measurements were defined as being in the range of 5 CDC standard deviation scores for weight and height. Features from maternal pregnancy were binned in alignment with the routine pregnancy tests schedule in Israel. Specific features of interest such as antibiotic prescriptions, ethnicity, and socioeconomic status surrogates were devised manually based on domain knowledge. Altogether, 943 features were devised for each child.
The characteristics of the Study Cohort and features used are summarized in Table 2.1, below.
The outcome for the models was the obesity status of children at 5 to 6 years of age. Obesity status was defined in accordance with health care professionals in Israel, using the CDC BMI reference percentiles. Cutoffs for normal weight, overweight, and obesity were determined using the CDC's standard thresholds of the 85th percentile for overweight and 95th percentile for obesity. Using other percentiles curves such as, but not limited to, the World Health Organization (WHO) WFL, and WHO BMI provided similar estimates of obesity risk as the CDC percentiles at 5 years of age.
Childhood Obesity Prediction Model
In this Example, Gradient Boosting trees were trained for providing the prediction. Trees allow nonlinear and multiple feature interactions to be captured, which may be important in obtaining an accurate prediction model. The parameters of the model were tuned using cross-validation on the training set. As stringent tests, both temporal and geographical validations were used, thus testing the performance of the model for distribution shifts over time and geographic location. The temporal validation set contained the most recent year in which the data were available. The geographical validation set contained all the clinics in the most populated and multiethnic city in Israel, Jerusalem. Unless stated otherwise, the reported results are on the temporal validation sets. Full results on both validation sets are available in Table 2.2, below.
As a baseline model for comparison the last WFL percentile routine checkup measurement available before 2 years of age was used, as current guidelines recommend that clinicians assess a child's current nutritional and obesity status by calculating WFL percentile or BMI percentile in children 0 to 2 years of age, or older than 2 years of age, respectively (Daniels et al. 2015). The WFL percentile thus emulates the information a caregiver has today to assess the current obesity status and future obesity risk of children younger than 2 years of age (Taveras et al. 2009). This variable also contains information of sex and age, as it standardizes by them. This variable itself is a predictor of the outcome, achieving an auROC of 0.749 and auPR of 0.223, and acts as a baseline to compare and improve upon.
Risk Factors Analysis from the Prediction Model
Risk factors were investigated by analyzing which features attribute to the model's prediction. To this end, the recently introduced SHAP (SHapley Additive exPlanation) method (Lundberg and Lee 2017; Lundberg et al. 2018) was used. The SHAP interprets the output of a machine learning model. A feature's Shapley value represents the average change in the model's output by conditioning on that feature when introducing features one at a time over all feature orderings. Shapley values were calculated individually for every child's feature. A property of Shapley values is that they are additive, meaning that the Shapley values of a child's features add up to the predicted log-odds of obesity for that child. In this Example, this value was transformed for each feature and each child to obtain a relative risk score.
Feature attributions were thus analyzed at the individual level, by examining plots of the Shapley value as a function of the feature value for all individuals. This method allowed capturing non-linear and continuous relations between a feature's impact on the prediction and the feature's value. A vertical spread in such a plot implies interaction with other features in the model, which would not have been attainable using a linear model. Building a model with many correlated features (e.g., a child's weight measurement at adjacent time points) is bound to suffer from severe collinearity of the features, and consequently the feature attributions will be spread across these related features. To tackle this, the additive property of Shapley values was used. Adding up the Shapely values of related features provided an analysis on this group of features. This provided better estimates of relevant risk scores. Another use of the additive property allows adding features according to groups and analyzing the model globally by taking the mean over absolute Shapely values of all children in each group of features. This gives insight on the impact of a feature group.
BMI trajectories were first analyzed in early childhood in relation to obesity status at 13-14 years of age. A total of 382,132 children with 1,401,803 measurements were included in the analysis (
The transition of obesity status over the first 6 years of life for the 136,196 children that were included in our cohort was analyzed. Obesity status was defined for each child at two time-points: the last available routine checkup before 2 years of age and at 5-6 years of age (
In accordance with some embodiments of the present invention, a model was constructed for predicting the likelihood for children at 0-2 years of age to develop childhood obesity at 5 to 6 years of age. The discrimination performance of the model was evaluated using the area under the receiver operating (auROC) and precision-recall (auPR) curves (
The model of the present embodiments outputs calibrated continuous risk probabilities. Applying a clinical decision thereafter (for example, a nutritional intervention) can vary between individuals and depend on the costs and benefits of the action, both clinically and economically. Decision curves (Vickers and Elkin 2006) offer a graphical tool to analyze clinical utility of adopting a new risk prediction model. The curves contain information that can guide clinicians to make decisions based on the risk thresholds, and based on the tradeoffs (costs and benefits) of their decision to treat. The costs and benefits can be translated into a function of the optimal threshold probability. In this Example, clinical utility was analyzed by constructing decision curves (
The discrimination results (auPR) of the model of the present embodiments were further analyzed on different subpopulations of children (
As earlier detection of childhood obesity may be more beneficial and allow earlier interventions, the ability to construct a prediction model for childhood obesity at the age 5-6 years of age was analyzed in the following time points: pre-birth, birth, 6 months, 1 year and 1.5 years of age. The effect of the child's age at prediction and the model discrimination performance is presented in
An analysis of feature attributions was performed using Shapley values. The results of the analysis are shown in
Analysis of the relative importance of different groups of features at different ages of applying the predictor revealed that the most influential features at birth are anthropometric measurements of the siblings, mother and father. Following these, the influence of the child's own anthropometrics measurements becomes more substantial and is roughly equal to the contribution of all other features in 1 years of age. Laboratory tests, drugs prescriptions and diagnoses have smaller relative influence, which decreases as the data on the child's anthropometrics accumulates (
Using information on pharmaceutical prescriptions, the effect of in utero and early life antibiotic exposure was also analyzed. 83,627 children (80%) had at least one antibiotic prescription in the first 2 years of life. The analysis revealed that antibiotic exposure in utero and in the first two years of life and age of first exposure to antibiotic had no effect on obesity risk at 5-6 years of age (
Based on the observation that infant routine checkups, family anthropometric measurements, and ethnicity contribute most to the predictive power of the model, a simple prediction model was established based on a set of self-assessed questions that parents can easily fill out at different time points up to 2 years of age in order to assess their child's risk of obesity. This model achieved an auROC of 0.798 and auPR of 0.296, compared to 0.749 and 0.223, respectively, for the baseline model.
This Example demonstrates a diagnostic prediction model for pediatric obesity at 5-6 years of age based on a comprehensive nationwide EHR encompassing over 10 years of children and familial data. Overweight 5-year-olds are four times more likely to become obese later in life compared to normal-weight children, and weight in this age is considered to be a good indicator of the child's future metabolic health. The target age of prediction model presented in this Example is also supported by a recently published observation on children BMI trajectories (Geserick et al. 2018), which was also replicated in our cohort, showing 2 to 6 years of age as the maximal BMI acceleration time period. The model is therefore designed to identify children at risk prior to this critical time window, in which mature eating patterns become more developed as children reduce breast milk or formula consumption. In addition, the analysis of the transition in obesity status in the first 6 years of life revealed that most obese children had normal weight at infancy, underscoring the importance of building a tool that allows clinicians to identify high risk infants that are considered to have a normal weight at infancy but will develop obesity, as they will constitute the majority of obese children in the future.
The model presented in this Example achieved an auROC of 0.803 and auPR of 0.304. Further Analysis of prediction performance on subpopulations of the cohort demonstrated robustness in discrimination performance across the entire pediatric population, including children with complex chronic diseases. Unlike previous studies (Hammond et al. 2019), the results presented in this Example were similar for boys and girls. Additional models were further devised for predicting obesity prior to two years of age. High impact of family anthropometric measurements in determining future obesity risk of the child was demonstrated. This Example showed that a prediction model constructed pre-birth, which is mainly based on family anthropometric measurements has very similar performance of predicting at 1 years of age based on the child's last available weight and length measurements. A simple self-assessed questionnaire for childhood obesity prediction pre-birth achieved an auROC of 0.798 and auPR of 0.296.
The technique presented in this Example has several advantages over previous studies. The technique presented in this Example include full data on both the child, from pregnancy to 5-6 years of age, and his family, and is the first to be validated both temporally and geographically at different clinics on a national level, thus representing a wide target population. The technique presented in this Example is the first to assess clinical utility by constructing decision curves. To date, there are no clinical guidelines defining the risk threshold for obesity prediction. The definition of this threshold may be influenced by many factors, including the characteristics of the proposed intervention, the availability of resources for intervention and the prevalence of obesity in the target population, and will impact the sensitivity and specificity of the prediction model. The decision curve analysis presented in this Example may thus help in determining risk thresholds and the clinical usefulness of the model for different interventions.
The mechanisms involved in the development of obesity in children are complex and include genetic, environmental, and developmental factors. The large cohort of Israeli children represents a diverse and multi-ethnic population with genetic heterogeneity. Not surprisingly, many of the variables found to be important in the model were directly related to the child's previous anthropometric measurements. Familial anthropometric measurements, including paternal, maternal and sibling's BMI were also important, in line with previous studies showing associations between these variables and childhood obesity. Among familial data, sibling's BMI had the highest impact on the prediction model, most likely due to both genetic and environmental influences.
There is evidence that uterine environment may cause a permanent influence on fetus future health, and may lead to enhanced susceptibility to diseases later in life. This concept is defined as ‘gestational programming’ of the fetus, and is thought to be mediated by Epigenetic mechanisms (Desai et al. 2015; Desai and Hales 1997). The data on maternal pregnancy, including lab tests, diagnoses and medications was used to analyze associations of these features to obesity status of the offspring at 5-6 years of age. One of the most prominent features in pregnancy was maternal blood glucose values (
The role of the gut microbiota in obesity has been vastly studied in recent years (Castaner et al. 2018). Microbiome composition undergoes many changes during the first years of life (Stewart et al. 2018). Antibiotics, which are frequently prescribed in the pediatric population (Chai et al. 2012), can significantly alter the microbiome composition (Robinson and Young 2010). Therefore, several recent studies assessed the relationship between antibiotic usage in early life and childhood obesity. These resulted in conflicting findings (Shao et al. 2017). The large sample size and the data on antibiotic prescriptions in pregnancy and infancy used in this Example allowed to explore this association. The analysis presented in this Example revealed that while the vast majority (80%) of the cohort received antibiotics at least once by the age of 2 years of age, antibiotic exposure in utero and in the first two years of life, and age of first exposure to antibiotic, had no observed impact on the obesity risk at 5-6 years of age.
The data used in This Example is from a retrospective observational EHR. These may suffer from potential biases and are affected by a variety of healthcare processes. Sampling bias was minimized by choosing children based on the schedule of routine measurements of weight and height, which includes both measurements at 0-2 years of age and a measurement at 5-6 years of age.
It is noted that while the prediction model presented in this Example is based on data of Israeli children, the validation process, which included both a temporal and a geographical validation, the well-known universal risk factors for childhood obesity that were found in the analysis of the model, and the striking similarity of the analysis on BMI trajectories to an independent, recently published German cohort (Geserick et al. 2018), indicates that the results may be generalized to other populations as well.
Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.
All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting. In addition, any priority document(s) of this application is/are hereby incorporated herein by reference in its/their entirety.
This application claims the benefit of priority under 35 USC 119(e) of U.S. Provisional Patent Application No. 62/882,623 filed on Aug. 5, 2019, the contents of which are all incorporated by reference as if fully set forth herein in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| 62882623 | Aug 2019 | US |
