Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review

Sepsis is a life-threatening condition resulting from a dysregulated immune response to infection, often leading to organ failure and death. Early detection is vital, as delays significantly worsen outcomes. In recent years, the integration of artificial intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has shown great promise in enhancing early sepsis detection by identifying digital biomarkers from large-scale clinical datasets. This systematic review analyzes and synthesizes existing ML/DL approaches applied to sepsis prediction, with an emphasis on intensive care unit (ICU) settings. A total of 80 studies were included, covering diverse data sources (e.g., MIMIC-III, eICU), feature selection methods, algorithm types, preprocessing techniques, and evaluation metrics. The models ranged from traditional techniques like logistic regression and decision trees to advanced architectures such as LSTM, transformers, and ensemble methods. A key contribution of this review is the inclusion of a forest plot summarizing reported AUC and sensitivity values from selected studies, offering a comparative visual of diagnostic performance. This helps highlight the relative effectiveness of different models and provides insights into their generalizability across clinical datasets. The review also discusses challenges related to model interpretability, ethical considerations, and the lack of external and temporal validation in many studies. It further identifies trends such as the use of real-time EHR data, patient-specific model development, and explainability tools like SHAP for clinician trust. By mapping out methodological strengths and limitations in current research, this work provides actionable recommendations for future studies and clinical deployment. The review contributes to the development of more robust, interpretable, and clinically relevant ML/DL models for early sepsis detection and improved patient care outcomes.

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