Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care.

ABSTRACT: Familial hypercholesterolaemia (FH) is a common inherited disorder, causing lifelong elevated low-density lipoprotein cholesterol (LDL-C). Most individuals with FH remain undiagnosed, precluding opportunities to prevent premature heart disease and death. Some machine-learning approaches improve detection of FH in electronic health records, though clinical impact is under-explored. We assessed performance of an array of machine-learning approaches for enhancing detection of FH, and their clinical utility, within a large primary care population. A retrospective cohort study was done using routine primary care clinical records of 4,027,775 individuals from the United Kingdom with total cholesterol measured from 1 January 1999 to 25 June 2019. Predictive accuracy of five common machine-learnin

SUBMITTER: Akyea RK 

PROVIDER: S-EPMC7603302 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature