Identification of Patients with Heart Failure in Large Datasets

被引：3

作者：

Kadosh, Bernard S. ^{[1
]}

Katz, Stuart D. ^{[1
]}

Blecker, Saul ^{[2
,3
,4
]}

机构：

[1] NYU, Sch Med, Dept Med, Leon H Charney Div Cardiol, New York, NY 10016 USA

[2] NYU, Sch Med, Dept Populat Hlth, New York, NY 10016 USA

[3] NYU, Sch Med, Dept Med, New York, NY 10016 USA

[4] NYU Langone Hlth, Ctr Healthcare Innovat & Delivery Sci, New York, NY USA

来源：

HEART FAILURE CLINICS | 2020年 / 16卷 / 04期

关键词：

Heart failure; Machine learning; Natural language processing; Phenomapping; ELECTRONIC HEALTH RECORDS; EJECTION FRACTION; RISK; HOSPITALIZATIONS; CLASSIFICATION; CHALLENGES; DIAGNOSIS; READMISSIONS; VALIDATION; GUIDELINES;

D O I：

10.1016/j.hfc.2020.05.001

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

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. © 2020 Elsevier Inc.

引用

页码：379 / 386

页数：8

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