Predicting the Onset of Sepsis Using Vital Signs Data: A Machine Learning Approach

Abstract

Sepsis is a major cause of mortality among hospitalized patients. Existing sepsis prediction methods face limitations due to their reliance on laboratory results and Electronic Medical Records (EMRs). This work aimed to develop a sepsis prediction model utilizing continuous vital signs monitoring, offering an innovative approach to sepsis prediction. Data from 48,886 Intensive Care Unit (ICU) patient stays were extracted from the Medical Information Mart for Intensive Care -IV dataset. A machine learning model was developed to predict sepsis onset based solely on vital signs. The model’s efficacy was compared with the existing scoring systems of SIRS, qSOFA, and a Logistic Regression model. The machine learning model demonstrated superior performance at 6 hrs prior to sepsis onset, achieving 88.1% sensitivity and 81.3% specificity, surpassing existing scoring systems. This novel approach offers clinicians a timely assessment of patients’ likelihood of developing sepsis.