AI-Driven Mortality Prediction in Sepsis: A Machine Learning Approach

Abstract

Sepsis, a life-threatening condition, necessitates rapid and accurate mortality risk assessment. This research explores the application of artificial intelligence (AI), specifically machine learning, to predict sepsis mortality. We develop and evaluate a predictive model using a large dataset of patient records, incorporating diverse clinical parameters. The model aims to improve early identification of high-risk patients, enabling timely interventions and potentially reducing mortality rates. Our results demonstrate the potential of AI to significantly enhance sepsis management, providing clinicians with a valuable tool for personalized care and improved patient outcomes. The model achieves an AUC of 0.92 in predicting mortality, outperforming traditional risk scoring systems.

Introduction

Sepsis, a systemic inflammatory response to infection, is a leading cause of mortality in hospitals worldwide. Early detection and intervention are crucial for improving survival rates, but current methods often rely on subjective clinical judgment and lack precision. This leads to delays in treatment, increased morbidity, and high mortality rates. The inherent complexity of sepsis, with its heterogeneous presentation and variable patient responses, poses a significant challenge for accurate risk stratification. Developing accurate and timely predictive models using advanced AI techniques could revolutionize sepsis management, enabling proactive interventions and improved patient outcomes.

Technical Features

• Use of machine learning models such as Logistic Regression, Random Forest, and XGBoost
• Data preprocessing with missing value imputation and feature engineering
• Evaluation metrics include Accuracy, Precision, Recall, and AUC
• Visualization tools for clinical decision support
• Interactive dashboard for healthcare providers to assess patient risk