Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events | Zendy

Li-Heng Fu | Zendy; Chris Knaplund | Zendy; Kenrick Cato | Zendy; Adler Perotte | Zendy; MinJeoung Kang | Zendy; Patricia C. Dykes | Zendy; David J. Albers | Zendy; Sarah Collins Rossetti | Zendy

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Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events

Author(s) -

Li-Heng Fu,

Chris Knaplund,

Kenrick Cato,

Adler Perotte,

MinJeoung Kang,

Patricia C. Dykes,

David J. Albers,

Sarah Collins Rossetti

Publication year - 2021

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/ocab111

Subject(s) - timestamp , computer science , discriminative model , artificial intelligence , electronic health record , machine learning , recurrent neural network , random forest , artificial neural network , logistic regression , data mining , health care , real time computing , economic growth , economics

To propose an algorithm that utilizes only timestamps of longitudinal electronic health record data to classify clinical deterioration events.

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