Pulmonary fibrosis is one of the most severe long-term consequences of COVID-19. Corticosteroid treatment increases the chances of recovery; unfortunately, it can also have side effects. Therefore, we aimed to develop prediction models for a personalized selection of patients benefiting from corticotherapy. The experiment utilized various algorithms, including Logistic Regression, k-NN, Decision Tree, XGBoost, Random Forest, SVM, MLP, AdaBoost, and LGBM. In addition easily human-interpretable model is presented. All algorithms were trained on a dataset consisting of a total of 281 patients. Every patient conducted an examination at the start and three months after the post-COVID treatment. The examination comprised a physical examination, blood tests, functional lung tests, and an assessment of health state based on X-ray and HRCT. The Decision tree algorithm achieved balanced accuracy (BA) of 73.52%, ROC-AUC of 74.69%, and 71.70% F1 score. Other algorithms achieving high accuracy included Random Forest (BA 70.00%, ROC-AUC 70.62%, 67.92% F1 score) and AdaBoost (BA 70.37%, ROC-AUC 63.58%, 70.18% F1 score). The experiments prove that information obtained during the initiation of the post-COVID-19 treatment can be used to predict whether the patient will benefit from corticotherapy. The presented predictive models can be used by clinicians to make personalized treatment decisions.

This article presents a comprehensive and multistage approach to the development of the user experience (UX) for an mHealth application targeting older adult patients with chronic diseases, specifically chronic heart failure and chronic obstructive pulmonary disease. The study adopts a mixed methods approach, incorporating both quantitative and qualitative components. The underlying hypothesis posits that baseline medicine adherence knowledge (measured by the MARS questionnaire), beliefs about medicines (measured by the BMQ questionnaire), and level of user experience (measured by the SUS and UEQ questionnaires) act as predictors of adherence change after a period of usage of the mHealth application. However, contrary to our expectations, the results did not demonstrate the anticipated relationship between the variables examined. Nevertheless, the qualitative component of the research revealed that patients, in general, expressed satisfaction with the application. It is important to note that the pilot testing phase revealed a notable prevalence of technical issues, which may have influenced participants’ perception of the overall UX. These findings contribute to the understanding of UX development in the context of mHealth applications for older adults with chronic diseases and emphasise the importance of addressing technical challenges to enhance user satisfaction and engagement.

Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a pathogen responsible for one of the most massive pandemics in modern history. In certain patients with prolonged resorption of pulmonary involvement, organizing pneumonia develops which may lead to irreversible fibrotic damage of the lungs. According to some studies, corticosteroid treatment increases the chance for successful recovery. However, the distinction between patients who would benefit from corticotherapy, and those, who would recover spontaneously, is unclear. This paper introduces an artificial intelligence-based recommendation system for a personalised selection of patients for corticotreatment. In this study, 101 patients were enrolled. Every patient conducted an examination at the start and 3 months after the post-COVID treatment. It included physical examination, blood tests, functional lung tests, and health state based on the high-resolution computed tomography scan results. The proposed methodology recommends the application of CS and achieved balanced accuracy 86.18% whether a patient will recover or not without CS medication. The study identifies the most accurate algorithm and the most significant attributes for this prediction. This paper also introduces a simplified and easy human-interpretable model, which reaches 83.23% balanced accuracy.