Performance comparison of artificial intelligence models in predicting 72-h emergency department unscheduled return visits

TabNet outperformed traditional machine learning approaches in predicting 72-h ED URVs, offering potential for improved risk stratification in emergency care settings.

Authors

Lunxian Tang
Yeon Ho You (ORCID: https://orcid.org/0000-0001-5998-2510)
Lumin Fan (ORCID: https://orcid.org/0000-0001-7408-644X)
Chongjun Fan
Hongyan Xiong (ORCID: https://orcid.org/0000-0002-9003-281X)
X. Zuo

Institutions

University of Shanghai for Science and Technology (CN)
University of Banja Luka (BA)
Shanghai Open University (CN)
Shanghai East Hospital (CN)
Shanghai University of Medicine and Health Sciences (CN)

Publication Details

Journal: Frontiers in Public Health
Published: 2025-12-19
DOI: https://doi.org/10.3389/fpubh.2025.1609206
Primary Topic: Emergency and Acute Care Studies
Type: article
Field-Weighted Citation Impact: 0.00

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article

Performance comparison of artificial intelligence models in predicting 72-h emergency department unscheduled return visits

Lunxian Tang, Yeon Ho You, Lumin Fan, Chongjun Fan et al.

Dec 19, 2025

Frontiers in Public Health

Emergency and Acute Care Studies

article

Performance comparison of artificial intelligence models in predicting 72-h emergency department unscheduled return visits

Lunxian Tang, Yeon Ho You, Lumin Fan, Chongjun Fan, Hongyan Xiong, X. Zuo

December 19, 2025article en

Abstract

TabNet outperformed traditional machine learning approaches in predicting 72-h ED URVs, offering potential for improved risk stratification in emergency care settings.

Frontiers in Public HealthVol. 13

University of Shanghai for Science and Technology (CN), University of Banja Luka (BA), Shanghai Open University (CN), Shanghai East Hospital (CN), Shanghai University of Medicine and Health Sciences (CN)

Openalex Percentile: Top 43%

Emergency and Acute Care Studies

DOI Open Access PDF OpenAlex

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