Automated Methods for Detection of Sepsis: A...

Automated Methods for Detection of Sepsis: A Comprehensive Review

Author Name : Rahul Dubey, Himanshu Singh

ABSTRACT For every hour that sepsis is not identified, patient survival is significantly reduced, and it remains a leading cause of in-hospital mortality. This study covers recent advances in machine learning (ML) and deep learning (DL) for early sepsis detection (ESD) using physiological time-series data and elec-tronic health records (EHRs). Using logistic regression, random forests, gradient boosting machines, and more recent deep archi-tectures like transformers, temporal convolutional networks, and long short-term memory (LSTM) networks, we systematically evaluate research. Important results demonstrate that DL models consistently outperform traditional scoring systems (e.g., SOFA, NEWS) by adding complex, nonlinear connections and temporal dependencies in vital signs, laboratory values, and clinical notes. However, there are still problems, including high false-alarm rates, poor model interpretability, substantial data imbalance, and a lack of prospective validation across various intensive care settings. Emerging methods like attention mechanisms and SHAP based explanations have boosted clinician trust, even if federated learning has promise for multi-institutional collabo-ration without data sharing. Even when retrospective investiga-tions obtained area under the curve (AUC) values more than 0.85, the research shows a substantial gap between algorithmic performance and real clinical use. We conclude that in order to translate predictive accuracy into useful bedside aids and lower sepsis mortality, future initiatives must prioritize explainable AI interfaces, prospective randomized trials, and consistent benchmarking.