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A Dynamic Risk Assessment Approach For Cooperative Medical Cyber-Physical Systems

  • Medical cyber-physical systems (MCPS) emerged as an evolution of the relations between connected health systems, healthcare providers, and modern medical devices. Such systems combine independent medical devices at runtime in order to render new patient monitoring/control functionalities, such as physiological closed loops for controlling drug infusion or optimization of alarms. Despite the advances regarding alarm precision, healthcare providers still struggle with alarm flooding caused by the limited risk assessment models. Furthermore, these limitations also impose severe barriers on the adoption of automated supervision through autonomous actions, such as safety interlocks for avoiding overdosage. The literature has focused on the verification of safety parameters to assure the safety of treatment at runtime and thus optimize alarms and automated actions. Such solutions have relied on the definition of actuation ranges based on thresholds for a few monitored parameters. Given the very dynamic nature of the relevant context conditions (e.g., the patient’s condition, treatment details, system configurations, etc.), fixed thresholds are a weak means for assessing the current risk. This thesis presents an approach for enabling dynamic risk assessment for cooperative MCPS based on an adaptive Bayesian Networks (BN) model. The main aim of the approach is to support continuous runtime risk assessment of the current situation based on relevant context and system information. The presented approach comprises (i) a dynamic risk analysis constituent, which corresponds to the elicitation of relevant risk parameters, risk metric building, and risk metric management; and (ii) a runtime risk classification constituent, which aims to analyze the current situation risk, establish risk classes, and identify and deploy mitigation measures. The proposed approach was evaluated and its feasibility proved by means of simulated experiments guided by an international team of medical experts with a focus on the requirements of efficacy, efficiency, and availability of patient treatment.

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Author:Fabio Luiz Leite JuniorORCiD
URN (permanent link):urn:nbn:de:hbz:386-kluedo-66239
DOI:https://doi.org/10.26204/KLUEDO/6623
Advisor:Peter Liggesmeyer, Ricardo João Cruz-CorreiaORCiD
Document Type:Doctoral Thesis
Language of publication:English
Publication Date:2021/09/01
Date of first Publication:2021/06/11
Publishing Institute:Technische Universität Kaiserslautern
Granting Institute:Technische Universität Kaiserslautern
Acceptance Date of the Thesis:2021/06/11
Date of the Publication (Server):2021/10/26
Tag:dependable systems; software engineering
GND-Keyword:dependable systems; risk management; software engineering
Number of page:XVIII, 192
Faculties / Organisational entities:Fachbereich Informatik
CCS-Classification (computer science):D. Software / D.2 SOFTWARE ENGINEERING (K.6.3) / D.2.0 General (K.5.1)
DDC-Cassification:0 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Licence (German):Creative Commons 4.0 - Namensnennung, nicht kommerziell (CC BY-NC 4.0)