SepSentinel: An Early Warning System for Sepsis Based on Wearable Biosensors and Machine Learning

SepSentinel is an open-source prototype project that uses machine learning models to analyze biomarker data collected by wearable devices for early non-invasive detection of sepsis. The system supports real-time risk assessment, a visual dashboard, and alert mechanisms, providing a complete proof-of-concept solution for the medical monitoring field. Keywords: Sepsis, Machine Learning, Biosensors, Wearable Devices, Medical AI, Early Warning, Health Monitoring, Python.

Sepsis is a systemic inflammatory response syndrome caused by infection, and it is one of the leading causes of death in ICUs worldwide, affecting millions of people each year with a mortality rate of 20%-40%. Early identification and intervention are crucial, but traditional diagnosis relies on blood tests and clinical assessments, which take several hours. The pathology of sepsis involves immune responses and metabolic disorders; abnormalities in key biomarkers (lactate, IL-6, pH value) precede symptoms, but traditional tests cannot monitor continuously, and wearable sensors provide a possible solution to this problem.

SepSentinel adopts a modular design, with core components including: 1. Biomarker definition module (standardized metadata); 2. Sensor data simulation (generates 60-minute disease deterioration sequences); 3. Risk assessment model (rule-based scoring system, logistic regression ML model with 99% accuracy); 4. Visualization tools (Matplotlib charts, risk dashboard, Streamlit web interface, terminal menu); 5. Multi-level alert mechanism (WARNING/CRITICAL levels).

The project is developed using Python 3.10+, relying on libraries such as scikit-learn (ML algorithms), pandas (data processing), matplotlib (visualization), and Streamlit (web application). The code structure is clear, modules are independently encapsulated, the data loader supports CSV/Excel formats, and it has an automatic column detection function.

1. Early warning value: Continuous monitoring captures changes missed by traditional tests, reducing mortality; 2. Reduced medical costs: Non-invasive monitoring reduces the need for blood tests; 3. Technical verification: Demonstrates the complete process of medical AI (from data collection to deployment); 4. Open-source contribution: Provides an extensible platform to promote technological iteration and innovation.

The development roadmap is divided into five phases: Basic modules → ML models → Real-time dashboard → Real dataset integration → Multi-patient tracking; currently in the fourth phase. Limitations: Relies on unvalidated simulated data, limited biomarkers, and the model's generalization ability needs testing.

Future directions: Integrate more sensor data (heart rate, body temperature, etc.), explore deep learning models, integrate with hospital systems, and conduct clinical trial verification. Summary: SepSentinel is an innovative open-source project that combines wearables and ML to provide a promising path for early sepsis detection. Although it needs to move from prototype to product, its architecture and methods provide a reference for medical AI.