Automated Detection and Mitigation of Dependability Failures in Healthcare Scenarios through Digital Twins

TL;DR

M-GENGAR is presented, a methodology based on a closed-loop Digital Twin (DT) paradigm for dependability assurance of medical CPSs that stabilizes patient vital metrics at least as effectively as human decision-making, while maintaining relevant metrics 20% closer to nominal healthy values on average.

Abstract

Medical Cyber-Physical Systems (CPSs) integrating Patients, Devices, and healthcare personnel (Physicians) form safety-critical PDP triads whose dependability is challenged by system heterogeneity and uncertainty in human and physiological behavior. While existing clinical decision support systems support clinical practice, there remains a need for proactive, reliability-oriented methodologies capable of identifying and mitigating failure scenarios before patient safety is compromised. This paper presents M-GENGAR, a methodology based on a closed-loop Digital Twin (DT) paradigm for dependability assurance of medical CPSs. The approach combines Stochastic Hybrid Automata modeling, data-driven learning of patient dynamics, and Statistical Model Checking with an offline critical scenario detection phase that integrates model-space exploration and diversity analysis to systematically identify and classify scenarios violating expert-defined dependability requirements. M-GENGAR also supports the automated synthesis of mitigation strategies, enabling runtime feedback and control within the DT loop. We evaluate M-GENGAR on a representative use case study involving a pulmonary ventilator. Results show that, in 87.5% of the evaluated scenarios, strategies synthesized through formal game-theoretic analysis stabilize patient vital metrics at least as effectively as human decision-making, while maintaining relevant metrics 20% closer to nominal healthy values on average.

Figures (9)

• Figure 1: PDP triad used as a running example showing interactions between the agents and the environment: an arrow from agent $a$ to $b$ represents an event that $a$ triggers directed to $b$. Controllable events are in blue, uncontrollable but observable events are in solid black, while uncontrollable and not directly observable events are represented by dashed arrows.
• Figure 2: Overview of proposed methodology, highlighting the three phases (dashed boxes), the data flow between phase activities (solid boxes), and the actors involved in activities requiring human input. The DT alignment loop is in blue.
• Figure 3: Example of physician-device SHA.
• Figure 4: Detail of learned patient SHA having $11$ locations.
• Figure 5: Physician-device PTGA used in the running example. Solid blue edges are the physician's controllable actions $\sigma_1, \dots,\sigma_n \in \Sigma_\mathrm{o, c}$. Dashed edges are uncontrollable actions.

Theorems & Definitions (12)

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