Identification of Patients with Heart Failure in Large Datasets

Abstract

Large registries, administrative data, and the electronic health record (EHR) offer opportunities to identify patients with heart failure, which can be used for research purposes, process improvement, and optimal care delivery. Identification of cases is challenging because of the heterogeneous nature of the disease, which encompasses various phenotypes that may respond differently to treatment. The increasing availability of both structured and unstructured data in the EHR has expanded opportunities for cohort construction. This article reviews the current literature on approaches to identification of heart failure, and looks toward the future of machine learning, big data, and phenomapping.

Keywords: Heart failure; Machine learning; Natural language processing; Phenomapping.