Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit

This study developed an interpretable ML triage model using vital signs, demographics, medical history, and chief complaints, proving more effective than traditional models in predicting ICU admission. Interpretable ML aids clinical decisions during triage.

Authors

Wenqi Shu
Xuan Zhang (ORCID: https://orcid.org/0000-0001-5727-7981)
Hongyan Liu (ORCID: https://orcid.org/0000-0001-8215-2160)
Chong Wei (ORCID: https://orcid.org/0000-0001-9690-8555)
Zheng Liu (ORCID: https://orcid.org/0000-0001-8597-3810)

Institutions

First Hospital of China Medical University (CN)
China Medical University (CN)

Publication Details

Journal: PLoS ONE
Published: 2025-02-18
DOI: https://doi.org/10.1371/journal.pone.0317819
Citations: 17
Primary Topic: Emergency and Acute Care Studies
Type: article
Field-Weighted Citation Impact: 38.31

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article

Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit

Wenqi Shu, Xuan Zhang, Hongyan Liu, Chong Wei et al.

Feb 18, 2025

17 citations

PLoS ONE

Emergency and Acute Care Studies

38.31

article

Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit

Wenqi Shu, Xuan Zhang, Hongyan Liu, Chong Wei, Zheng Liu

February 18, 2025article en

17 citations

Abstract

This study developed an interpretable ML triage model using vital signs, demographics, medical history, and chief complaints, proving more effective than traditional models in predicting ICU admission. Interpretable ML aids clinical decisions during triage.

PLoS ONEVol. 20(2)

First Hospital of China Medical University (CN), China Medical University (CN)

Openalex Percentile: Top 0%

Emergency and Acute Care Studies

38.31

DOI Open Access PDF OpenAlex

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