Comparison and interpretability of machine learning models to predict severity of chest injury

Sujay Kulshrestha; Dmitriy Dligach; Cara Joyce; Richard Gonzalez; Ann P. O'Rourke; Joshua M. Glazer; Anne Stey; Jacqueline M. Kruser; Matthew M. Churpek; Majid Afshar

doi:10.1093/jamiaopen/ooab015

Comparison and interpretability of machine learning models to predict severity of chest injury

Sujay Kulshrestha, Dmitriy Dligach, Cara Joyce, Richard Gonzalez, Ann P. O'Rourke, Joshua M. Glazer, Anne Stey, Jacqueline M. Kruser, Matthew M. Churpek, Majid Afshar

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	ooab015
Journal	JAMIA Open
Volume	4
Issue number	1
DOIs	https://doi.org/10.1093/jamiaopen/ooab015
State	Published - Jan 1 2021

ASJC Scopus Subject Areas

Health Informatics

Keywords

interpretability
machine learning
trauma surgery

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10.1093/jamiaopen/ooab015

Cite this

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title = "Comparison and interpretability of machine learning models to predict severity of chest injury",

keywords = "interpretability, machine learning, trauma surgery",

author = "Sujay Kulshrestha and Dmitriy Dligach and Cara Joyce and Richard Gonzalez and O'Rourke, {Ann P.} and Glazer, {Joshua M.} and Anne Stey and Kruser, {Jacqueline M.} and Churpek, {Matthew M.} and Majid Afshar",

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T1 - Comparison and interpretability of machine learning models to predict severity of chest injury

AU - Kulshrestha, Sujay

AU - Dligach, Dmitriy

AU - Joyce, Cara

AU - Gonzalez, Richard

AU - O'Rourke, Ann P.

AU - Glazer, Joshua M.

AU - Stey, Anne

AU - Kruser, Jacqueline M.

AU - Churpek, Matthew M.

AU - Afshar, Majid

PY - 2021/1/1

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KW - interpretability

KW - machine learning

KW - trauma surgery

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DO - 10.1093/jamiaopen/ooab015

M3 - Article

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VL - 4

JO - JAMIA Open

JF - JAMIA Open

IS - 1

M1 - ooab015

ER -

Comparison and interpretability of machine learning models to predict severity of chest injury

ASJC Scopus Subject Areas

Keywords

Access to Document

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