Personalized Preventive Corticosteroid Medication Recommendation System for Postacute COVID-19 Treatment

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.