A machine learning evaluation of patient characteristics associated with prescribing of guideline-directed medical therapy for heart failure.

ABSTRACT:

Introduction/background

Patients with heart failure and reduced ejection fraction (HFrEF) are consistently underprescribed guideline-directed medications. Although many barriers to prescribing are known, identification of these barriers has relied on traditional a priori hypotheses or qualitative methods. Machine learning can overcome many limitations of traditional methods to capture complex relationships in data and lead to a more comprehensive understanding of the underpinnings driving underprescribing. Here, we used machine learning methods and routinely available electronic health record data to identify predictors of prescribing.

Methods

We evaluated the predictive performance of machine learning algorithms to predict prescription of four types of medications fo

SUBMITTER: Kim R 

PROVIDER: S-EPMC10321403 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature