Accuracy is not enough: explainable boosting machine model and identification of candidate biomarkers for real-time sepsis risk assessment in the emergency department
The EBM model accurately predicts sepsis risk based on clinically relevant biomarkers. The model's high performance and inherent transparency can foster trust among clinicians and facilitate its integration into emergency department workflows for real-time decision support.
Authors
- Amal Khalifa Alkhalifa (ORCID: https://orcid.org/0000-0002-7273-4041)
- Sarah A. Alzakari (ORCID: https://orcid.org/0000-0001-8265-2421)
- Mohammadreza Aghaei (ORCID: https://orcid.org/0000-0001-5735-3825)
- Cemil Çolak (ORCID: https://orcid.org/0000-0001-5406-098X)
- Fatma Hilal Yağın (ORCID: https://orcid.org/0000-0002-9848-7958)
- Umran Aygun
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- University of Freiburg (DE)
- Inonu University (TR)
- Norwegian University of Science and Technology (NO)
- Malatya Turgut Özal Üniversitesi (TR)
- Malatya Devlet Hastanesi (TR)
- Turgut Özal University (TR)
- Lakehead University (CA)
Publication Details
- Journal
- BMC Emergency Medicine
- Published
- 2025-11-28
- DOI
- https://doi.org/10.1186/s12873-025-01402-w
- Citations
- 1
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Princess Nourah Bint Abdulrahman University