Predictive Modeling of Sepsis Onset in ICUs Using Real-Time Wearable Sensor Data and LSTM Networks

Abstract

Sepsis remains a leading cause of morbidity and mortality in intensive care units (ICUs), with early detection significantly improving patient outcomes. Conventional approaches to sepsis prediction rely on episodic clinical measurements and rule-based scoring systems such as SOFA or SIRS, which are limited by static thresholds and delayed response to physiological deterioration. The advent of continuous monitoring through wearable sensors and the application of advanced deep learning techniques particularly Long Short-Term Memory (LSTM) networks offer a paradigm shift toward real-time, data-driven prediction of sepsis onset.

This study presents a comprehensive exploration of predictive modeling for early sepsis detection using continuous wearable sensor data streams. We develop and evaluate an LSTM-based architecture that integrates multi-modal physiological signals (heart rate, respiratory rate, temperature, blood oxygen saturation, and electrodermal activity) to predict sepsis onset several hours before clinical diagnosis. We emphasize data preprocessing, temporal pattern extraction, feature representation, model interpretability, and evaluation metrics relevant to clinical deployment.

Our findings suggest that LSTM networks can capture complex temporal dependencies inherent in physiological time series, outperforming traditional machine learning models in predictive accuracy and lead-time detection. The article also discusses ethical, infrastructural, and translational considerations in integrating predictive sepsis models into ICU workflows.