Predicting COVID-19-Induced Lung Damage Based on Machine Learning Methods

In this paper, we consider the course of the coronavirus disease (COVID-19) in human patients. We investigate anamnesis, examination, and clinical analysis data, as well as other features that can affect the severity and mortality of COVID-19. Based on these features, we develop a set of machine learning and statistical models that can predict the severity of the coronavirus disease and its outcome for inpatients and outpatients. The main contribution of this work is the development of the CT Calculator service, which is integrated in the Moscow city medical information system. This service allows one to assesses the degree of changes in the lung tissue of COVID-19 patients in an express mode without computed tomography (CT) scan, as well as predict the degree of lung damage. The developed machine learning models make it possible to determine the degree of risk for mild and severe forms of the coronavirus disease depending on various factors.