Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter data.

ABSTRACT:

Background

Cardiovascular outcomes for people with familial hypercholesterolaemia can be improved with diagnosis and medical management. However, 90% of individuals with familial hypercholesterolaemia remain undiagnosed in the USA. We aimed to accelerate early diagnosis and timely intervention for more than 1·3 million undiagnosed individuals with familial hypercholesterolaemia at high risk for early heart attacks and strokes by applying machine learning to large health-care encounter datasets.

Methods

We trained the FIND FH machine learning model using deidentified health-care encounter data, including procedure and diagnostic codes, prescriptions, and laboratory findings, from 939 clinically diagnosed individuals with familial hypercholesterolaemia (395 of whom had a molec

SUBMITTER: Myers KD 

PROVIDER: S-EPMC8086528 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature