Speaker: Bruno Guindani
24 Febbraio 2026 | 14:15
DEIB, Sala Seminari "A. Alario" (Ed. 21)
Per maggiori informazioni: Silvia Cascianelli | silvia.cascianelli@polimi.it
Sommario
Tuesday, February 24th 2026 at 2:15 pm a new appointment of Data Science Seminars: Bioinformatics focus will take place in DEIB "Alessandra Alario" Seminar Room (Building 21) organized by the Data Science for Bioinformatics group.The seminar will be held by Bruno Guindani, Research Assistant at DEIB, on the following subject: "Automated Generation of Digital Twins and Specifications for Healthcare".
The talk presents results from the SAFEST project (PRIN). Medical cyber-physical systems (CPSs) that integrate 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.
The talk introduces GENGAR, a methodology based on a closed-loop Digital Twin (DT) paradigm for the dependability assurance of medical CPSs. It combines Stochastic Hybrid Automata modeling, data-driven learning of patient dynamics, fuzzing-based model-space exploration, and clustering in an offline critical-scenario detection phase. In a second phase, it provides automated synthesis of mitigation strategies, enabling runtime feedback and control within the DT loop.
GENGAR is evaluated through a representative use case involving a pulmonary ventilator. Results show that, in most evaluated scenarios, strategies synthesized through formal game-theoretic analysis stabilize patient vital metrics at least as effectively as human decision-making, while keeping relevant metrics closer to nominal healthy values on average. The talk will also briefly introduce MARACTUS, a related methodology that automates the extraction of medical procedures and guidelines into machine-readable representations. This action is achieved by transforming unstructured clinical documents into analyzable action models for integration into model-driven pipelines and clinical decision support systems.