Open AccessUtilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events
Author(s) -
Li-Heng Fu,
Chris Knaplund,
Kenrick Cato,
Adler J. Perotte,
MooIl 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.