Survival Analysis of COVID-19 Patients in Russia Using Machine Learning

The current pandemic can likely have several waves and will require a major effort to save lives and provide optimal treatment. The efficient clinical resource planning and efficient treatment require identification of risk groups and specific clinical features of the patients. In this study we develop analyze mortality for COVID19 patients in Russia. We identify comorbidities and risk factors for different groups of patients including cardiovascular diseases and therapy. In the study we used a Russian national COVID registry, that provides sophisticated information about all the COVID-19 patients in Russia. To analyze Features importance for the mortality we have calculated Shapley values for the "mortality" class and ANN hidden layer coefficients for patient lifetime. We calculated the distribution of days spent in hospital before death to show how many days a patient occupies a bed depending on the age and the severity of the disease to allow optimal resource planning and enable age-based risk assessment. Predictors of the days spent in hospital were calculated using Pearson correlation coefficient. Decisions trees were developed to classify the patients into the groups and reveal the lethality factors.