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

• URL: http://arxiv.org/abs/2602.21037v1
• Date: Tue, 24 Feb 2026 15:56:20 GMT
• Title: Automated Detection and Mitigation of Dependability Failures in Healthcare Scenarios through Digital Twins
• Authors: Bruno Guindani, Matteo Camilli, Livia Lestingi, Marcello M. Bersani,
• Abstract summary: M-GENGAR is a methodology based on a closed-loop Digital Twin (DT) paradigm for dependability assurance of medical CPSs.<n>M-GENGAR supports the automated synthesis of mitigation strategies, enabling runtime feedback and control within the DT loop.<n>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.
• Score: 3.188134462843442
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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