Predicting brain function status changes in critically ill patients via Machine learning | Zendy

Chao Yan | Zendy; Cheng Gao | Zendy; Ziqi Zhang | Zendy; Wencong Chen | Zendy; Bradley Malin | Zendy; E. Wesley Ely | Zendy; Mayur B. Patel | Zendy; You Chen | Zendy

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Predicting brain function status changes in critically ill patients via Machine learning

Author(s) -

Chao Yan,

Cheng Gao,

Ziqi Zhang,

Wencong 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.

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