Machine learning for early detection and prediction of sepsis: explainability and key sepsis biomarkers representation—A systematic review

OBJECTIVE: To systematically review machine learning-based sepsis prediction studies, examining model explainability and the extent to which explanations reflect key sepsis biomarkers. DATA SOURCES: Following the PRISMA guidelines, we reviewed the titles, abstracts, and full texts. The search was conducted in four major bibliographic databases with publication dates from January 1, 2019 to July 16, 2025. STUDY SELECTION: The included studies provided a clear definition of sepsis based on the Sepsis-3 criteria and involved critically ill adult human subjects. DATA EXTRACTION AND SYNTHESIS: Two authors (IP and AKa) independently reviewed and assessed each study. Using statistical methods, we assessed study quality and explainability trends. RESULTS: A total of 37 studies were included. Our analysis revealed a notable temporal increase (≈67% greater odds per year) in the use of explainability methods in sepsis prediction models. However, key sepsis biomarkers (procalcitonin or C-reactive protein) were not among the top predictive features, highlighting a gap between the model output and known sepsis pathophysiology. DISCUSSION: Model attributions often mirror what electronic health records measure most consistently (vital signs) rather than what is most biologically specific, partly due to the high missingness and irregular sampling of CRP/PCT in public datasets. Heterogeneity in feature selection and reliance on local datasets limit generalizability, while sparse code/data sharing constrains reproducibility. CONCLUSION: This review newly quantifies the rise of explainability use in sepsis prediction and identifies a consistent gap between model explanations and key sepsis biomarkers, providing a foundation for future work to bridge data-driven insights with sepsis pathophysiology. SYSTEMATIC REVIEW REGISTRATION NUMBER: CRD420251101470.