Machine Learning for Real-Time Prediction of Sepsis Onset

Abstract

Sepsis is a life-threatening condition that requires early intervention for improved patient outcomes. Machine learning (ML) algorithms have shown promise in predicting sepsis onset, but real-time prediction remains a challenge. This study develops ML algorithms for real-time prediction of sepsis onset, aiming to enable early intervention. Using a dataset of patient records, various ML models are trained and evaluated for their predictive performance. The results demonstrate the feasibility of real-time sepsis prediction using ML, highlighting the potential impact on patient care and outcomes.