Machine learning for early detection of sepsis: an internal and temporal validation study | Zendy

Armando Bedoya | Zendy; Joseph Futoma | Zendy; Meredith E. Clement | Zendy; Kristin Corey | Zendy; Nathan Brajer | Zendy; Anthony Lin | Zendy; Morgan Simons | Zendy; Michael Gao | Zendy; Marshall Nichols | Zendy; Suresh Balu | Zendy; Katherine Heller | Zendy; Mark Sendak | Zendy; Cara O’Brien | Zendy

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Machine learning for early detection of sepsis: an internal and temporal validation study

Author(s) -

Armando Bedoya,

Joseph Futoma,

Meredith E. Clement,

Kristin Corey,

Nathan Brajer,

Anthony Lin,

Morgan Simons,

Michael Gao,

Marshall Nichols,

Suresh Balu,

Katherine Heller,

Mark Sendak,

Cara O’Brien

Publication year - 2020

Publication title -

jamia open

Language(s) - English

Resource type - Journals

ISSN - 2574-2531

DOI - 10.1093/jamiaopen/ooaa006

Subject(s) - sepsis , artificial intelligence , random forest , logistic regression , systemic inflammatory response syndrome , medicine , deep learning , machine learning , statistic , early warning score , computer science , recurrent neural network , artificial neural network , statistics , emergency medicine , mathematics

Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice.

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