ArticleScientific reports2026
Selective classification with machine learning uncertainty estimates improves ACS prediction: a retrospective study in the prehospital setting.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Artificial intelligence in prehospital assessment of acute coronary syndrome: a scoping review.BMC emergency medicine · 2026Article
- From Biosignals to Bedside: A Review of Real-Time Edge Machine Learning for Wearable Health Monitoring.Bioengineering (Basel, Switzerland) · 2026Review
- Elevated serum asprosin and ANGPTL8 gene expression as novel biomarkers for the diagnosis and prognosis of acute coronary syndrome.Frontiers in cardiovascular medicine · 2025Article
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Abstract
Accurate identification of acute coronary syndrome (ACS) in the prehospital setting is important for timely treatments that reduce damage to the compromised myocardium. Current machine learning approaches lack sufficient performance to safely rule-in or rule-out ACS. Our goal is to identify a method that bridges this gap. To do so, we retrospectively evaluate two promising approaches, an ensemble of gradient boosted decision trees (GBDT) and selective classification (SC) on consecutive patients transported by ambulance to the ED with chest pain and/or anginal equivalents. On the task of ACS classification with 23 prehospital covariates, we found the fusion of the two (GBDT+SC) improves the best reported sensitivity and specificity by 8% and 23% respectively. Accordingly, GBDT+SC is safer than current machine learning approaches to rule-in and rule-out of ACS in the prehospital setting.
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