Abstract
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.
Highlights
Any medical decision, be it diagnosis or a patient’s response to treatments, is a difficult task
The data for this study was collected from real records of COVID-19 patients in ICU at Guy’s and St Thomas’ National Health Service (NHS) Foundation Trust
Fuzzy Decision Tree (FDT) and Fuzzy Random Forest (FRF) are proposed for the classification as decision tree based classifier with well interpretability and visibility of the result
Summary
Be it diagnosis or a patient’s response to treatments, is a difficult task. The classification methods used for the medical decision-making should be able to complete missing values, as well as cope with both losses of information and data during the transformation process Such classification methods usually developed based approaches of Machine Learning [1,2,3,4]. In this paper we propose to consider other classifiers, which are good interpretability: FDT and FRF These classifiers are developed to facilitate the decision-making in timing of tracheostomy for prolonged respiratory wean in critically ill COVID-19 patients. The decision tree based C4.5 method has been inducted and used for the prediction of the tracheostomy time In this paper this method is developed by the application of fuzzy classifiers (in particular, FDT and FRF). These classifiers are developed based on real records of COVID-19 patients and take into consideration all specifics of this data.
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