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April 3, 2026Clinical and Experimental Emergency MedicineOpen Access

Integrating the interpretable machine learning Score For Emergency Risk Prediction (SERP) with emergency department triage to better predict 30-Day mortality

Yuan Feng Yohei Okada Stephanie Fook-Chong Yilin Ning Kennth Boon Kiat Tan Marcus Eng Hock Ong

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Key Points

This research aims to enhance the prediction of 30-day mortality using machine learning within emergency department triage.
Integrated the SERP algorithm with existing emergency department triage protocols
Utilized PACS for data input and analysis
Assessed prediction accuracy for 30-day mortality outcomes
Improved accuracy in predicting 30-day mortality compared to traditional methods
Demonstrated enhanced performance when SERP was included in triage processes

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Implication

Integration of ML-based SERP improves 30-day mortality prediction in emergency department triage, suggesting better outcomes.

Abstract

Integrating ML-based SERP with PACS improved 30-day mortality prediction in ED triage.

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Cite This Study

Feng et al. (2026) studied this question.

synapsesocial.com/papers/69d1fd8ea79560c99a0a3944 https://doi.org/10.15441/ceem.25.253

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