Possibilities of information systems for prediction of outcomes of new coronavirus infection COVID-19

Abstract

The pandemic of coronavirus infection COVID-19 (Coronavirus Disease 2019), caused by a new strain of coronavirus SARSCoV-2 (severe acute respiratory syndrome coronavirus 2), has caused high mortality worldwide. The clinical manifestations of COVID-19 are nonspecific. Diagnostics includes clinical, laboratory and radiological data. The importance of introducing information systems into medical practice in order to improve the quality of medical care is noted. It is stated that the development of medical artificial intelligence is associated with the development of artificial intelligence programs designed to help the clinic in making a diagnosis, prescribing treatment, as well as predicting the outcome of the disease. Such systems include artificial neural networks, fuzzy expert systems, and hybrid intelligent systems. The article analyzes data from a number of studies on the use of artificial intelligence for diagnosing COVID-19, predicting the risk of mortality and studying risk factors for severe course and lethal outcome in various groups. Using clusters of predictors, models have been developed to predict mortality and understand the relationship of various characteristics and diseases with mortality from COVID-19. The article also summarizes the key factors that worsen the prognosis for COVID-19. Scales for detecting or predicting the development of COVID-19-induced “cytokine storm” are marked as a separate item.

Keywords

• new coronavirus infection covid-19
• digital technologies
• artificial intelligence
• neural networks
• risk factors
• scale
• forecasting