Fuzzy Decision Tree Based Method in Decision-Making of COVID-19 Patients' Treatment

A new method in decision-making of timing of tracheostomy in COVID-19 patients is developed and discussed in this paper. Tracheostomy is performed in critically ill coronavirus disease (COVID-19) patients. The timing of tracheostomy is important for anticipated prolonged ventilatory wean when levels of respiratory support were favorable. The analysis of this timing has been implemented based on classification method. One of principal conditions for the developed classifiers in decision-making of timing of tracheostomy in COVID-19 patients was a good interpretation of result. Therefore, the proposed classifiers have been developed as decision tree based because these classifiers have very good interpretability of result. The possible uncertainty of initial data has been considered by the application of fuzzy classifiers. Two fuzzy classifiers as Fuzzy Decision Tree (FDT) and Fuzzy Random Forest (FRF) have been developed for the decision-making in tracheostomy timing. The evaluation of proposed classifiers and their comparison with other show the efficiency of the proposed classifiers. FDT has best characteristics in comparison with other classifiers.

Reflection of the COVID-19 pandemic in mass media

The paper analyzes the 'reflection' of the COVID-19 theme in the mass media of the Republic of Kazakhstan and the Russian Federation. Used a corpus of mass media of about 1 million publications and WHO data. Using topic modeling and analysis of correlation dependences, both the similarity and some difference in the publication activity of mass media were established. In particular, in both countries, media activity correlates with the absolute indicators of the number of new infected and the number of deaths. Correlation coefficient is 0.6-0.8. However, mass media take into account the number of positive rate, reproduction rate weaker. Correlation coefficient is 0.4-0.6. In the Russian media, there is a high correlation (0.75) with changes in measures to limit the spread of infection taken by the state, while in Kazakhstan this correlation is much lower (0.4). © 2021 IEEE.