Accuracy is not enough: explainable boosting machine model and identification of candidate biomarkers for real-time sepsis risk assessment in the emergency department

Abstract Background Sepsis poses a significant threat in emergency settings, necessitating tools for early and interpretable risk assessment. This study aimed to develop a robust explainable boosting machine (EBM) model, one of the explainable artificial intelligence (XAI) technologies, to construct...

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Bibliografische Detailangaben

Hauptverfasser:	Fatma Hilal Yagin, Umran Aygun, Cemil Colak, Amal K. Alkhalifa, Sarah A. Alzakari, Mohammadreza Aghaei
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	BMC 2025-11-01
Schriftenreihe:	BMC Emergency Medicine
Schlagworte:	Sepsis Machine learning Explainable artificial intelligence Explainable boosting machine Biomarker
Online-Zugang:	https://doi.org/10.1186/s12873-025-01402-w
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