Extracting Deep Phenotypes for Chronic Kidney Disease Using Electronic Health Records | Zendy

Duc Thanh Anh Luong | Zendy; Van Dinh Tran | Zendy; Wilson Pace | Zendy; Miriam Dickinson | Zendy; Joseph A. Vassalotti | Zendy; Jennifer K. Carroll | Zendy; Matthew WithiamLeitch | Zendy; Min Yang | Zendy; Nikhil Satchidanand | Zendy; Elizabeth W. Staton | Zendy; Linda S. Kahn | Zendy; Varun Chandola | Zendy; Chester H. Fox | Zendy

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Extracting Deep Phenotypes for Chronic Kidney Disease Using Electronic Health Records

Author(s) -

Duc Thanh Anh Luong,

Van Dinh Tran,

Wilson Pace,

Miriam Dickinson,

Joseph A. Vassalotti,

Jennifer K. Carroll,

Matthew WithiamLeitch,

Min Yang,

Nikhil Satchidanand,

Elizabeth W. Staton,

Linda S. Kahn,

Varun Chandola,

Chester H. Fox

Publication year - 2017

Publication title -

egems (generating evidence and methods to improve patient outcomes)

Language(s) - English

Resource type - Journals

ISSN - 2327-9214

DOI - 10.5334/egems.226

Subject(s) - subtyping , kidney disease , medicine , disease , confounding , health records , electronic health record , covariate , bioinformatics , machine learning , computer science , biology , health care , economics , programming language , economic growth

The large dataset of EHRs can be used to identify deep phenotypes retrospectively. Directions for further expansion of the model are also discussed.

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