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Medical Cyber-Physical Systems (MCPS) are context-aware, life-critical systems with patient safety as the main concern, demanding rigorous processes for validation to guarantee user requirement compliance and specification-oriented correctness. In this article, we propose a model-based approach for early validation of MCPS, focusing on promoting reusability and productivity. It enables system developers to build MCPS formal models based on a library of patient and medical device models, and simulate the MCPS to identify undesirable behaviors at design time. Our approach has been applied to three different clinical scenarios to evaluate its reusability potential for different contexts. We have also validated our approach through an empirical evaluation with developers to assess productivity and reusability. Finally, our models have been formally verified considering functional and safety requirements and model coverage.

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Background Controlling the COVID-19 outbreak in Brazil is a challenge due to the population’s size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources. Objective The purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies. Methods Raw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models’ performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances. Results Gender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model. Conclusions The DT classification model can effectively (with a mean accuracy≥89.12%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing.

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Safety and effectiveness are crucial quality attributes for insulin infusion pump systems. Therefore, regulatory agencies require the quality evaluation and approval of such systems before the market to decrease the risk of harm, motivating the usage of a formal Model-Based Approach (MBA) to improve quality. Nevertheless, using a formal MBA increases costs and development time because it requires expert knowledge and thorough analyses of behaviors. We aim to assist the quality evaluation of such systems in a cost-effective and time-efficient manner, providing re-usable project artifacts by applying our proposed approach (named MBA with CPN—MBA/CPN). We defined a Coloured Petri nets MBA and a case study on a commercial insulin infusion pump system to verify and validate a reference model (as a component of MBA/CPN), describing quality assessment scenarios. We also conducted an empirical evaluation to verify the productivity and reusability of modelers when using the reference model. Such a model is relevant to reason about behaviors and quality evaluation of such concurrent and complex systems. During the empirical evaluation, using the reference model, 66.7% of the 12 interviewed modelers stated no effort, while 8.3% stated low effort, 16.7% medium effort, and 8.3% considerable effort. Based on the modelers’ knowledge, we implemented a web-based application to assist them in re-using our proposed approach, enabling simulation-based training. Although a reduced number of modelers experimented with our approach, such an evaluation provided insights to improve the MBA/CPN. Given the empirical evaluation and the case study results, MBA/CPN showed to be relevant to assess the quality of insulin infusion pump systems.

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