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Ioannis Papapanagiotou, Apostolos Karalis, Stelios Kokkoris | International Journal of Medical Informatics | (2026)
Key Takeaways
Plain English Takeaway
Many computer programs can now help doctors spot sepsis early, but these programs often focus on easy-to-measure signs instead of the most important blood markers for sepsis.
Study Aim
The main goal of this paper is to review recent studies that use machine learning (computer programs that learn from data) to predict sepsis (a serious infection response) in adults. The authors specifically want to see how well these programs explain their predictions and whether they use the most important blood markers for sepsis.
Simply put: The paper checks if computer tools for spotting sepsis use the right medical clues and explain their decisions clearly.
Study Design
The authors conducted a systematic review, following PRISMA guidelines (a standard for reviewing studies). They searched four major databases for studies published between January 2019 and July 2025. Only studies that used the Sepsis-3 definition (a standard way to define sepsis) and included critically ill adult patients were chosen. Two reviewers independently checked each study for quality and how well they explained their results.
Simply put: The researchers carefully looked at recent studies about computer programs that predict sepsis in very sick adults.
Findings
The review found that more studies are now using explainability methods (ways to show how computer programs make decisions) in sepsis prediction, with about 67% more studies doing this each year. However, the most important blood markers for sepsis, like procalcitonin and C-reactive protein, were rarely used by these programs. Instead, the programs mostly relied on vital signs (like heart rate and blood pressure), which are measured more often in hospitals. This is partly because the key blood markers are not always recorded in public datasets. The authors also note that differences in which features are chosen and the use of local data make it hard to apply these findings everywhere. They recommend better data sharing and more focus on using the right medical clues in future research.
Simply put: The study found that computer tools for sepsis prediction are getting better at explaining their choices, but they often miss the most important blood tests for sepsis.
Abstract
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.
Referenced In
Created: May 5, 2026