Design and Evaluation of an AI-Powered Clinical Decision Support System for Early Diagnosis of Sepsis in Intensive Care Units

Abstract

Sepsis is a major cause of mortality in intensive care units (ICUs), and early diagnosis significantly improves survival outcomes. Traditional scoring systems such as SIRS and SOFA are limited in sensitivity and timeliness. This paper presents the design and performance evaluation of an Artificial Intelligence-powered Clinical Decision Support System (AI-CDSS) aimed at the early diagnosis of sepsis using ICU patient data. Utilizing supervised machine learning models and retrospective datasets, the system was trained and validated for accuracy, sensitivity, and lead-time performance.Focusing on data from before 2022, particularly the widely used MIMIC-III database, this study evaluates the effectiveness of random forest and logistic regression models for sepsis prediction. The AI-CDSS demonstrated a lead time of 5.6 hours before clinical recognition, with an AUROC exceeding 0.85. The paper also reviews literature prior to 2022 and highlights how AI-driven CDSS can be better integrated into critical care workflows through interpretability and usability enhancements.