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

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

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 ap...

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Bibliographic Details
Main Authors: Peter Hill, Daniel Jonsson, Jakob Lederman, Peter Bolin, Veronica Vicente
Format: Article
Language:English
Published: BMC 2025-06-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Emergency medical services
Response time
Machine learning
Time-Sensitive conditions
Breathing problems
Clinical decision support systems
Online Access:https://doi.org/10.1186/s12911-025-03046-z
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https://doi.org/10.1186/s12911-025-03046-z

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