Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualization

Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualization

Abstract Objectives To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening and early intervention for sepsis patients. Methods This multicenter retrospective cohort study analyzed electronic medical records of sepsis pa...

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Bibliographic Details
Main Authors: Songchang Shi, Lihui Zhang, Shujuan Zhang, Jinyang Shi, Donghuang Hong, Siqi Wu, Xiaobin Pan, Wei Lin
Format: Article
Language:English
Published: BMC 2025-01-01
Series:Journal of Translational Medicine
Subjects:
Sepsis
Machine learning
Mortality
Prediction
Visualization
Online Access:https://doi.org/10.1186/s12967-025-06102-4
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Summary:Abstract Objectives To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening and early intervention for sepsis patients. Methods This multicenter retrospective cohort study analyzed electronic medical records of sepsis patients using machine learning methods. We evaluated model performance in predicting sepsis outcomes within the first 24 h of ICU admission across US and Chinese healthcare settings. Results From 31 clinical features, machine learning models demonstrated significantly better predictive performance than traditional approaches for sepsis outcomes. While linear regression achieved low test scores (0.25), machine learning methods reached scores of 0.78 and AUCs above 0.8 in testing. Importantly, these models maintained robust performance (scores 0.63–0.77) in external validation. Conclusions The application of machine learning-based prediction models for sepsis could significantly improve patient outcomes through early detection and timely intervention in the critical first 24 h of ICU admission, supporting clinical decision-making.
ISSN:1479-5876

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