Machine learning prediction of therapy response

Abstract

A method comprising: receiving, for each of a plurality of subjects, each having a specified type of cardiovascular or cardiometabolic disease and receiving at least one specified therapy from a set of therapies for treating cardiovascular and cardiometabolic diseases, a first score representing a first genetic predisposition in said subject to respond to one or more of said set of therapies; at a training stage, training a machine learning model on a training set comprising: (i) all of said first scores, and labels associated with a response in each of said subjects to said at least one specified therapy; and at an inference stage, apply said trained machine learning model to a target said first score received with respect to a target subject, to predict a response in said target subject to at least one of said therapies in said set.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]This is a continuation of U.S. application Ser. No. 17 / 020,002, filed on Sep. 14, 2020. The contents of the above applications are all incorporated by reference as if fully set forth herein in their entirety.FIELD OF THE INVENTION[0002]The present invention relates to the field of machine learning.BACKGROUND OF THE INVENTION[0003]Hypertension (HTN) is the strongest modifiable risk factor for cardiovascular disease, and is the most common condition seen in the primary care setting. Globally, HTN is the leading cause of death and disability-adjusted life-years worldwide. In the United States, HTN affects ˜80 million individuals with over 5 million new diagnoses each year.[0004]Effective treatment for rapid reductions in blood pressure (BP) is important for survival in HTN subjects, as end organ damage can occur quickly. Unfortunately, BP control rates are very poor with only half of HTN subjects receiving treatment achieving control.[0005]E...

AI Technical Summary

Benefits of technology

[0019]In the response in each of the subjects and the target subject to the one or more specified therapies comprises at least one: lowering of SBP, lowering of DBP, lowering of blood glucose levels, lowering of blood lipids levels, improving of coronary heart disease symptoms, improving of heart failure symptoms, lowering weight, and improving of arrhythmia symptoms.

Problems solved by technology

Unfortunately, BP control rates are very poor with only half of HTN subjects receiving treatment achieving control.

However, there is a general lack of genetically-informed therapy decisions in clinical practice today.

Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the figures.

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