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