Real-Time Sepsis Prediction Through Continuous Telemonitoring: A Machine Learning-Based Clinical Framework

Abstract

Sepsis remains one of the leading causes of mortality worldwide, largely due to delayed recognition and inconsistent clinical monitoring. Real-time telemonitoring combined with machine learning has emerged as a transformative solution capable of detecting early physiological deterioration before clinical symptoms become severe. This research paper proposes a comprehensive machine-learning clinical framework for real-time sepsis prediction through continuous telemonitoring. The study integrates multiparametric physiological signals including heart rate, respiratory rate, SpO₂, temperature, blood pressure, perfusion index, and lactate trends, collected through wearable IoT devices and bedside clinical monitors. The proposed framework incorporates data preprocessing pipelines, multi-model prediction engines, and automated alert systems designed for remote clinical environments. The methodology consists of the integration of Random Forest, Gradient Boosting, and LSTM-based deep learning models applied on continuous streaming data. A hybrid ensemble is constructed to optimize sensitivity and reduce false negatives, which are critical in sepsis care. The case study evaluates the model implementation on a rural teleICU network covering 420 patients over a six-month period. Findings indicate that the hybrid model achieved 92.4% sensitivity, 89.1% specificity, and a lead time of 3.8 hours prior to clinical diagnosis. Data analysis reveals that heart rate variability, respiration-  SpO₂ coupling, and temperature-lactate interaction emerged as the most predictive biomarkers. This framework reduces diagnostic delays, enhances remote triage capabilities, and improves patient outcomes while empowering rural health systems with advanced AI-driven telemonitoring. The results demonstrate that telemonitoring-based ML frameworks can significantly enhance early sepsis detection, reduce ICU transfers, and enable timely clinical interventions with scalable and cost-efficient deployment.