Developing a Machine Learning-Based Predictive Model for Early Sepsis Diagnosis using Electronic Health Records

Abstract

Sepsis is a potentially fatal illness that needs to be identified quickly in order to enhance patient outcomes. Unfortunately, because of its non-specific symptoms, early detection can be difficult. Predictive models for early sepsis diagnosis could be developed using the wealth of data provided by electronic health records (EHRs). By examining EHR data and finding patterns linked to the onset of sepsis, machine learning (ML) algorithms have demonstrated encouraging promise in this field. The purpose of this project is to use EHR data to create and assess a machine learning (ML) predictive model for early sepsis detection. Our main goal will be to extract pertinent features— such as demographics, vital signs, test findings, and medication information—from easily accessible EHR data. To determine which machine learning technique performs best in terms of accuracy, sensitivity, and specificity, we will analyze and contrast a number of different models, including logistic regression, support vector machines, and random forests. Our model's performance will be compared to conventional sepsis scoring methods, and it will be assessed on a retrospective dataset of patients with confirmed sepsis cases. The ultimate objective of this research is to create a therapeutically applicable tool that will help medical personnel identify people who are at danger of sepsis early on. Early interventions, better patient outcomes, and lower healthcare expenditures can result from this.