Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems

• URL: http://arxiv.org/abs/2104.02545v1
• Date: Tue, 6 Apr 2021 14:36:33 GMT
• Title: Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems
• Authors: Xugui Zhou, Bulbul Ahmed, James H. Aylor, Philip Asare, Homa Alemzadeh
• Abstract summary: Medical Cyber-physical Systems (MCPS) are vulnerable to accidental or malicious faults that can target their controllers and cause safety hazards and harm to patients. This paper proposes a combined model and data-driven approach for designing context-aware monitors that can detect early signs of hazards and mitigate them.
• Score: 2.126171264016785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical Cyber-physical Systems (MCPS) are vulnerable to accidental or malicious faults that can target their controllers and cause safety hazards and harm to patients. This paper proposes a combined model and data-driven approach for designing context-aware monitors that can detect early signs of hazards and mitigate them in MCPS. We present a framework for formal specification of unsafe system context using Signal Temporal Logic (STL) combined with an optimization method for patient-specific refinement of STL formulas based on real or simulated faulty data from the closed-loop system for the generation of monitor logic. We evaluate our approach in simulation using two state-of-the-art closed-loop Artificial Pancreas Systems (APS). The results show the context-aware monitor achieves up to 1.4 times increase in average hazard prediction accuracy (F1-score) over several baseline monitors, reduces false-positive and false-negative rates, and enables hazard mitigation with a 54% success rate while decreasing the average risk for patients.

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