Work

Detecting Dependability Failures in Healthcare Scenarios via Digital Shadows

Year: 2025

Type: conference-paper

Abstract: In healthcare systems, practitioners are responsible for making decisions when a patient’s health, or even life, are at stake. Real-time data-driven modeling, analysis, and prediction approaches, such as the Digital Shadow (DS) paradigm, inform and support decision-makers in such critical situations. We introduce GENGAR, a DS-based methodology to identify critical scenarios in a patient-device-physician (PDP) triad with human agents and cyber-physical devices interacting under uncertainty. The proposed solution relies on automata-based modeling and formal analysis techniques to predict and inform the practitioner of critical contingencies that may compromise patient safety, enhancing the system’s dependability. In particular, it leverages automata learning to infer and evolve a realistic patient model from clinical logs. GENGAR then exploits mutational and searchbased fuzzing to generate scenarios and detect failure cases, i.e., those violating predefined dependability requirements. Failure scenarios are then filtered using qualitative criteria on clinical plausibility, yielding up to $60 \%$ realistic cases.

Source: IEEE International Symposium on Software Reliability Engineering (ISSRE)

DOI: 10.1109/issre66568.2025.00056

Authors: Bruno Guindani, Matteo Camilli, Livia Lestingi, Marcello Maria Bersani

Institution: Politecnico di Milano

Language: English

Topic: Formal Methods in Verification

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: Ministry of Education