Open AccessPredicting brain function status changes in critically ill patients via Machine learning
Author(s) -
Chao Yan,
Cheng Gao,
Ziqi Zhang,
Wen-Cong Chen,
Bradley Malin,
E. Wesley Ely,
Mayur B. Patel,
You Chen
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/ocab166
Subject(s) - generalizability theory , boosting (machine learning) , medicine , receiver operating characteristic , artificial intelligence , machine learning , confidence interval , gradient boosting , critically ill , intensive care , computer science , intensive care medicine , statistics , mathematics , random forest
In intensive care units (ICUs), a patient's brain function status can shift from a state of acute brain dysfunction (ABD) to one that is ABD-free and vice versa, which is challenging to forecast and, in turn, hampers the allocation of hospital resources. We aim to develop a machine learning model to predict next-day brain function status changes.