Advancing a machine learning-based decision support tool for pre-hospital assessment of dyspnoea by emergency medical service clinicians: a retrospective observational study

Advancing a machine learning-based decision support tool for pre-hospital assessment of dyspnoea by emergency medical service clinicians: a retrospective observational study

In Sweden with about 10 million inhabitants, there are about one million primary ambulance missions every year. Among them, around 10% are assessed by Emergency Medical Service (EMS) clinicians with the primary symptom of dyspnoea. The risk of death among these patients has been reported to be remarkably high, at 11,1% and 13,2%. The aim was to develop a Machine Learning (ML) model to provide support in assessing patients in pre-hospital settings and to compare them with established triage tools.

Wivica Kauppi, Henrik Imberg, Johan Herlitz, Oskar Molin, Christer Axelsson & Carl Magnusson

https://link.springer.com/article/10.1186/s12873-024-01166-9

https://link.springer.com/journal/12873

05 january 2025

Fajar Bagus W

PDF

English

Text