Fuzzy case-based reasoning approach for finding COVID-19 patients priority in hospitals at source shortage period

Abstract

In this research article, we introduced an algorithm to evaluate COVID-19 patients admission in hospitals at source shortage period. Many researchers have expressed their conclusions from different perspectives on various factors such as spatial changes, climate risks, preparedness, blood type, age and comorbidities that may be contributing to COVID-19 mortality rate. However, as the number of people coming to the hospital for COVID-19 treatment increases, the mortality rate is likely to increase due to the lack of medical facilities. In order to provide medical assistance in this situation, we need to consider not only the extent of the disease impact, but also other important factors. No method has yet been proposed to calculate the priority of patients taking into account all the factors. We have provided a solution to this in this research article. Based on eight key factors, we provide a way to determine priorities. In order to achieve the effectiveness and practicability of the proposed method, we studied individuals with different results on all factors. The sigmoid function helps to easily construct factors at different levels. In addition, the cobweb solution model allows us to see the potential of our proposed algorithm very clearly. Using the method we introduced, it is easier to sort high-risk individuals to low-risk individuals. This will make it easier to deal with problems that arise when the number of patients in hospitals continues to increase. It can reduce the mortality of COVID-19 patients. Medical professionals can be very helpful in making the best decisions.

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“…Contou et al 25 have reported that median survival time, of 73 out of 153 patients admitted to ICU’s being critically ill due to refractory respiratory failure, shock with multiorgan failure, cardiac death, and neurological death, was 14 days. Geetha et al 26 have shown that the average time to peak the severity of symptoms, since the symptoms began, was 7 days on average. Garcia-Garcia et al’s 18 estimations of the probability distribution of death since illness yielded a mean of 14.5 days and a median of 13.2 days.…”

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“…Its applications are covered, for instance, by Nasser et al [73] and Tayali and Timor [74]. The application of other methods can be found in the research of Geetha et al [75], Narayanamoorthy et al [76] and Ramya [77].…”

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“…Contou et al 16 have reported that the median survival time, of 73 out of 153 patients admitted to ICU's being critically ill due to refractory respiratory failure, shock with multiorgan failure, cardiac death, and neurological death, was 14 days. Geetha et al 17 have shown that the average time to peak the severity of symptoms, since the symptoms began, was 7 days on average. Hence, we take a conservative 7 to 14 day window for t De and t Ds from the day of showing up symptoms for individuals, identified as new cases, C, to be subjected to death.…”