A customizable deep learning model for nosocomial risk prediction from critical care notes with indirect supervision | Zendy

Travis R. Goodwin | Zendy; Dina DemnerFushman | Zendy

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A customizable deep learning model for nosocomial risk prediction from critical care notes with indirect supervision

Author(s) -

Travis R. Goodwin,

Dina DemnerFushman

Publication year - 2020

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocaa004

Subject(s) - disease , computer science , baseline (sea) , feature engineering , artificial intelligence , medicine , chart , health care , machine learning , intensive care medicine , emergency medicine , deep learning , statistics , oceanography , economic growth , economics , geology , mathematics

Reliable longitudinal risk prediction for hospitalized patients is needed to provide quality care. Our goal is to develop a generalizable model capable of leveraging clinical notes to predict healthcare-associated diseases 24-96 hours in advance.

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