Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission | Zendy

Nathan Brajer | Zendy; Brian Cozzi | Zendy; Michael Gao | Zendy; Marshall Nichols | Zendy; Mike Revoir | Zendy; Suresh Balu | Zendy; Joseph Futoma | Zendy; Jonathan Bae | Zendy; Noppon Setji | Zendy; Adrian F. Hernandez | Zendy; Mark Sendak | Zendy

Open Access

Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission

Author(s) -

Nathan Brajer,

Brian Cozzi,

Michael Gao,

Marshall Nichols,

Mike Revoir,

Suresh Balu,

Joseph Futoma,

Jonathan Bae,

Noppon Setji,

Adrian F. Hernandez,

Mark Sendak

Publication year - 2020

Publication title -

jama network open

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.278

H-Index - 39

ISSN - 2574-3805

DOI - 10.1001/jamanetworkopen.2019.20733

Subject(s) - medicine , prospective cohort study , emergency medicine , electronic health record , cohort , pediatrics , health care , economics , economic growth

Prospective and multisite retrospective evaluations of a machine learning model demonstrated good discrimination of in-hospital mortality for adult patients at the time of admission. The data elements, methods, and patient selection make the model implementable at a system level.

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