Early Sepsis Detection Using Neural Network

Abstract

Sepsis establishes as a dangerous condition from excessive body responses to infections and results in widespread inflammation before it triggers tissue damage and organ dysfunction. Sepsis detection remains a priority in medical settings because of its quick onset and difficult diagnosis evaluation. Therapeutic intervention which is delayed will dramatically reduce survival chances for patients. The research presents a deep learning solution built upon neural networks for determining sepsis onset through continuous streaming patient information. The model receives training through a wide dataset combining vital sign information (heart rate and blood pressure) with respiratory rate and temperature data and patient characteristic features. The network system detects sepsis-related early patterns to generate clinical alert signals for medical intervention. The research proves the model's usefulness for hospital real-time monitoring through accuracy testing and AUC-ROC measurements alongside recall criteria which immediately detect patients enabling better clinical care.