Revolutionizing Sepsis Diagnosis Using Machine Learning and Deep Learning Models: A Systematic Literature Review

Sepsis, a life-threatening disease characterized by the body’s severe response to infection, remains a worldwide health issue causing mortality and morbidity. The complexity and rapid growth of sepsis make it challenging to detect in its early phases using traditional techniques. Thus, machine learning (ml) and deep learning (dl) methods have emerged as promising tools, offering the potential to process vast amounts of electronic health data, detect patterns, and predict the onset of sepsis earlier than conventional techniques. This systematic review critically examines the use of ML and DL models for sepsis detection and prediction with application across diverse clinical datasets i.e. Electronic Health Records (EHRs), vital signs monitoring systems, and large-scale databases. Through a comprehensive search of the relevant literature, this review synthesizes findings from over 125 studies, exploring the effectiveness of various computational methods more than 1500. The process of systematic literature review SLR included accessing articles from IEEE, ACM, and Scopus, with deletion of duplicate papers, articles from languages other than English, and outdated studies that resulted in only 80 valid studies. These methods range from simpler algorithms i.e. decision trees and support vector machines, to other models i.e., neural networks and ensemble techniques. Each model's capacity to handle the complexity of sepsis data is thoroughly analyzed. Besides this, the review also highlights key challenges inside the field, data quality problems, the generalization of models throughout patients with different populations, and ethical considerations related. These challenges pose barriers to the adoption of ML and DL for sepsis recognition in real-world clinical settings. In conclusion, the study highlights the need for advanced feature engineering, the use of ensemble techniques, advancement of integrated and real-time sepsis prediction systems. Such models improve accuracy, robustness, and scalability of predictive models for effective interventions. By addressing the current limitations with refining, these models in sepsis identification could transform current clinical practice with improved clinical outcomes.