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