AI-Driven Early Warning System for Sepsis Deterioration in Post-Operative Cardiac Surgery Patients
DOI:
https://doi.org/10.15662/IJEETR.2026.0802204Keywords:
Sepsis, Artificial Intelligence, Internet of Things (IoT), Early Warning System, ICU Monitoring, Machine Learning.Abstract
Background: Sepsis represents a critical life-threatening condition characterized by dysregulated host response to infection, contributing significantly to intensive care unit mortality worldwide. Post-operative cardiac surgery patients face elevated sepsis risk due to invasive procedures, prolonged mechanical ventilation, and immunosuppression. Early physiological indicators often remain subtle and non-specific, challenging timely diagnosis through conventional monitoring approaches.
Objective: This study develops and evaluates an artificial intelligence-driven early warning system combining Internet of Things technology with machine learning algorithms for real-time sepsis detection in post-cardiac surgery patients.
Methods: The system integrates non-invasive wearable sensors measuring heart rate, heart rate variability, oxygen saturation, and body temperature. An ESP8266 microcontroller processes physiological signals and transmits data through WiFi connectivity. Machine learning algorithms establish patient-specific baseline profiles during post-operative stabilization, employing statistical modeling and anomaly detection to identify deviations indicating sepsis onset.
Results: Experimental prototype testing demonstrated detection accuracy of 94-96% with improved sensitivity and specificity compared to threshold-based monitoring systems. The adaptive baseline approach reduced false alarm rates while maintaining early detection capability. Real-time edge processing enabled response times under 2 seconds.
Conclusion: The proposed AI-driven monitoring system provides personalized, proactive sepsis detection with significant improvements in accuracy and responsiveness. Integration of IoT-based continuous monitoring with adaptive machine learning offers promising advancement toward reducing ICU mortality through earlier clinical intervention.
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