Uncovering nonlinear patterns in time-sensitive prehospital breathing emergencies: an exploratory machine learning study.

Journal: BMC medical informatics and decision making
Published Date: Jun 3, 2025

Abstract

BACKGROUND: Timely prehospital care is crucial for patients presenting with high-risk time-sensitive (HRTS) conditions. However, the interplay between response time and demographic factors in patients with breathing problems remains insufficiently understood. In this exploratory study, we applied machine learning (ML) to examine how emergency medical response time, age, and sex jointly influence the probability of encountering HRTS conditions.

Authors

  • Peter Hill
    Region Stockholm Health and Medical Care Administration, The Department for Specialized Care, Stockholm, Sweden. peter.hill@ki.se.
  • Daniel Jönsson
    Department of Periodontology, Malmö University, Malmö and Swedish Dental Service of Skane, Lund, Sweden.
  • Jakob Lederman
    Region Stockholm Health and Medical Care Administration, The Department for Specialized Care, Stockholm, Sweden.
  • Peter Bolin
    Region Stockholm Health and Medical Care Administration, The Department for Specialized Care, Stockholm, Sweden.
  • Veronica Vicente
    The Ambulance Medical Service in Stockholm (AISAB), Stockholm, Sweden.

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