An Interpretable Machine Learning Model for Early Multitemporal Prediction of Onset of Acute Kidney Injury in Intensive Care Unit Patients with Severe Trauma

Our study found ML models excel in diagnosing AKI risk in ICU trauma patients but have limited prognostic accuracy at 24-48 and 48-72 h post-admission. Further research is needed to improve this using time-series ML models with optimal windows.

Authors

Bingrui Gao
Jack Chen (ORCID: https://orcid.org/0000-0003-4693-5234)
Hongliang Jin
Yan Zhang (ORCID: https://orcid.org/0000-0002-4835-8541)

Institutions

New South Wales Department of Health (AU)
UNSW Sydney (AU)
The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
Chongqing Public Health Medical Center (CN)
Chongqing Medical University (CN)

Publication Details

Journal: Journal of Intensive Care Medicine
Published: 2025-10-29
DOI: https://doi.org/10.1177/08850666251390848
Primary Topic: Sepsis Diagnosis and Treatment
Type: article
Field-Weighted Citation Impact: 0.00

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article

An Interpretable Machine Learning Model for Early Multitemporal Prediction of Onset of Acute Kidney Injury in Intensive Care Unit Patients with Severe Trauma

Bingrui Gao, Jack Chen, Hongliang Jin, Yan Zhang

Oct 29, 2025

Journal of Intensive Care Medicine

Sepsis Diagnosis and Treatment

article

An Interpretable Machine Learning Model for Early Multitemporal Prediction of Onset of Acute Kidney Injury in Intensive Care Unit Patients with Severe Trauma

Bingrui Gao, Jack Chen, Hongliang Jin, Yan Zhang

October 29, 2025article en

Abstract

Our study found ML models excel in diagnosing AKI risk in ICU trauma patients but have limited prognostic accuracy at 24-48 and 48-72 h post-admission. Further research is needed to improve this using time-series ML models with optimal windows.

Journal of Intensive Care Medicine

New South Wales Department of Health (AU), UNSW Sydney (AU), The Affiliated Yongchuan Hospital of Chongqing Medical University (CN), Chongqing Public Health Medical Center (CN), Chongqing Medical University (CN)

Openalex Percentile: Top 58%

Sepsis Diagnosis and Treatment

DOI Source OpenAlex

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