Article

Application of concise machine learning to construct accurate and interpretable EHR computable phenotypes

2020-12-14

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Objective</h4> Electronic health records (EHRs) can improve patient care by enabling systematic identification of patients for targeted decision support. But, this requires scalable learning of computable phenotypes. To this end, we developed the feature engineering automation tool (FEAT) and assessed it in targeting screening for the underdiagnosed, under-treated disease primary aldosteronis...

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Literature Corpus work: 653bf33e-b0ff-596a-9ef3-6b3a5f1c9ea5
DOI: 10.1101/2020.12.12.20248005

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