Methods and systems for predicting a health condition of a human subject

An application server to predict a health condition of a first human subject, the application server comprising:

• one or more processors configured to:
• receive a measure of one or more physiological parameters associated with the first human subject, wherein the one or more physiological parameters comprise at least one of an age, a cholesterol level, a heart rate, a blood pressure, a breath carbon-dioxide concentration, a breath oxygen concentration, a stroke score, a blood creatinine level, a blood albumin level, a blood sodium level, a total blood count, a blood glucose/sugar level, a blood hemoglobin level, and/or a blood platelet count;
• extract a historical data comprising one or more records, wherein each of the one or more records includes a measure of the one or more physiological parameters associated with each of one or more second human subjects;
• cluster the historical data into a plurality of d-vines by sampling a plurality of latent variables based on a rank transformation of the historical data;
• estimate one or more latent variables from the plurality of latent variables based on:
• a first count indicative of a number of the plurality of d-vines,
• a second count indicative of a number of the one or more records,
• a first value that is representative of a number of the one or more records clustered into a d-vine from the plurality of d-vines, and
• a second value that is representative of a parameter utilizable to predict a third value, wherein the third value corresponds to a probability of clustering the historical data into the d-vine from the plurality of d-vines;
• generate the plurality of d-vines based on the estimated one or more latent variables from the plurality of latent variables;
• train a classifier based on the generated plurality of d-vines;
• and
• predict the health condition of the first human subject by utilizing the classifier based on the received measure of the one or more physiological parameters associated with the first human subject.

A computer program product for use with a computer, the computer program product comprising a non-transitory computer readable medium, wherein the non-transitory computer readable medium stores a computer program code for predicting a health condition of a first human subject, wherein the computer program code is executable by one or more processors to:

• receive a measure of one or more physiological parameters associated with a first human subject, wherein the one or more physiological parameters comprise at least one of an age, a cholesterol level, a heart rate, a blood pressure, a breath carbon-dioxide concentration, a breath oxygen concentration, a stroke score, a blood creatinine level, a blood albumin level, a blood sodium level, a total blood count, a blood glucose/sugar level, a blood hemoglobin level, and/or a blood platelet count;
• extract a historical data comprising one or more records, wherein each of the one or more records includes a measure of the one or more physiological parameters associated with each of one or more second human subjects;
• cluster the historical data into a plurality of d-vines by sampling a plurality of latent variables based on a rank transformation of the historical data;
• estimate one or more latent variables from the plurality of latent variables based on:
• a first count indicative of a number of the plurality of d-vines,
• a second count indicative of a number of the one or more records,
• a first value that is representative of a number of the one or more records clustered into a d-vine from the plurality of d-vines, and
• a second value that is representative of a parameter utilizable to predict a third value, wherein the third value corresponds to a probability of clustering the historical data into the d-vine from the plurality of d-vines;
• generate the plurality of d-vines based on the estimated one or more latent variables from the plurality of latent variables;
• train a classifier based on the generated plurality of d-vines;
• and
• predict a health condition of the first human subject by utilizing the classifier based on the received measure of the one or more physiological parameters associated with the first human subject.