Interpretable machine learning for predicting sepsis risk in emergency triage patients

Interpretable machine learning for predicting sepsis risk in emergency triage patients

Abstract The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive triage information beyond vital signs. This...

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Bibliographic Details
Main Authors: Zheng Liu, Wenqi Shu, Teng Li, Xuan Zhang, Wei Chong
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Sepsis
Triage
Emergency
Interpretable machine learning
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Online Access:https://doi.org/10.1038/s41598-025-85121-z
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https://doi.org/10.1038/s41598-025-85121-z

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