Explainable models for early sepsis detection
Yılmaz, A.; Keller, M.; Rossi, L. · Article a2
https://doi.org/10.1234/ajm.2026.012
Abstract
We present a calibrated gradient boosting pipeline for early sepsis prediction using routinely collected EHR features. The model improves AUROC while remaining interpretable for clinical triage teams.
Keywords
sepsis · machine learning · explainability · EHR
Cite this article
APA. Yılmaz, A., Keller, M., & Rossi, L. (2026). Explainable models for early sepsis detection. Anatolian Journal of Medicine, 12(2), 12–28. https://doi.org/10.1234/ajm.2026.012
MLA. Yılmaz, Ayşe, et al. “Explainable Models for Early Sepsis Detection.” Anatolian Journal of Medicine, vol. 12, no. 2, 2026, pp. 12–28.
Chicago. Yılmaz, Ayşe, Markus Keller, and Luca Rossi. 2026. “Explainable Models for Early Sepsis Detection.” Anatolian Journal of Medicine 12 (2): 12–28.