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Performance of Machine Learning Models for Sepsis and Stroke Detection Using EMS Data.

Lawrence H Brown1,2, Remle P Crowe3, Oleksandr Ivanov4

  • 1Dell Medical School, University of Texas at Austin, Austin, Texas.

Prehospital Emergency Care
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

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Machine learning models can identify sepsis and stroke in emergency medical services (EMS) data, improving early detection. This study shows the feasibility of applying hospital-trained models to prehospital electronic health records for better patient outcomes.

Area of Science:

  • Emergency Medicine
  • Data Science
  • Machine Learning

Background:

  • Early recognition of sepsis and stroke by emergency medical services (EMS) is crucial for improving patient triage, treatment, and outcomes.
  • Machine learning (ML) offers potential to enhance EMS sepsis and stroke screening by analyzing complex patterns in electronic health record (EHR) data.
  • Research applying ML to EMS data for these conditions remains limited.

Purpose of the Study:

  • To evaluate the feasibility and performance of ML models, originally designed for hospital emergency department (ED) triage data, when applied to EMS EHR data for sepsis and stroke detection.

Main Methods:

  • Retrospective analysis of linked EMS and ED records for adult patients transported to a single hospital.
  • Hospital ML models were adapted to EMS EHR data, incorporating vital signs, chief complaints, and impressions, with ED physician diagnoses as the reference standard.

Related Experiment Videos

  • Analyses included different prediction aggregation methods (majority vs. any prediction) and incorporation of EMS free-text narratives.

    • Main Results:

    • The primary ML model ('majority of predictions') achieved 68% sensitivity and 86% specificity for sepsis, and 71% sensitivity and 94% specificity for stroke.
    • The 'any prediction' approach increased sensitivity but decreased specificity.
    • Incorporating free-text narratives further enhanced sensitivity at the cost of specificity.

    Conclusions:

    • Applying ML models trained on ED data to prehospital EMS data is feasible for early sepsis and stroke identification.
    • Future research should focus on retraining models with EMS-specific data, prospective validation, and developing real-world implementation strategies.