Machine Learning Tool for Assessing Dyspnoea in Emergency Medical Services
Author Information
Author(s): Wivica Kauppi, Henrik Imberg, Johan Herlitz, Oskar Molin, Christer Axelsson, Carl Magnusson
Primary Institution: University of Borås
Hypothesis
Can a machine learning model improve the assessment of dyspnoea in pre-hospital settings compared to traditional triage tools?
Conclusion
The machine learning model using gradient boosting significantly outperformed traditional triage methods in predicting serious adverse events among patients with dyspnoea.
Supporting Evidence
- All machine learning models showed better performance than traditional triage tools.
- The gradient boosting algorithm had the best performance with a ROC AUC of 0.81.
- 80% of the data was used for model development and 20% for validation.
- 1,118 serious adverse events were identified among the study cohort.
- 78% of patients had a history of dyspnoea.
Takeaway
Researchers created a smart computer program to help ambulance workers figure out how serious patients with breathing problems are, and it worked better than older methods.
Methodology
This was a retrospective observational study analyzing data from 6,354 patients who called for an ambulance with dyspnoea as the main symptom.
Potential Biases
There is a risk of incomplete documentation due to the retrospective nature of the study.
Limitations
The study's data is from 2017, which may limit its relevance today, and it was conducted in a specific region of Sweden, affecting generalizability.
Participant Demographics
78% of participants were 65 years or older, and 44% were male.
Statistical Information
P-Value
p<0.05
Confidence Interval
95% CI 0.70–0.76 for RETTS-A, 95% CI 0.78–0.84 for gradient boosting
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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