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Neural Computing and Applications
Published in: Neural Computing and Applications 14/2022

27-01-2021 | S.I.: Healthcare Analytics

A fuzzy rule-based efficient hospital bed management approach for coronavirus disease-19 infected patients

Authors: Kalyan Kumar Jena, Sourav Kumar Bhoi, Mukesh Prasad, Deepak Puthal

Published in: Neural Computing and Applications | Issue 14/2022

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Abstract

Coronavirus disease-19 (COVID-19) is a very dangerous infectious disease for the entire world in the current scenario. Coronavirus spreads from one person to another person very rapidly. It spreads exponentially throughout the globe. Everyone should be cautious to avoid the spreading of this novel disease. In this paper, a fuzzy rule-based approach using priority-based method is proposed for the management of hospital beds for COVID-19 infected patients in the worst-case scenario where the number of hospital beds is very less as compared to the number of COVID-19 infected patients. This approach mainly attempts to minimize the number of hospital beds as well as emergency beds requirement for the treatment of COVID-19 infected patients to handle such a critical situation. In this work, higher priority has given to severe COVID-19 infected patients as compared to mild COVID-19 infected patients to handle this critical situation so that the survival probability of the COVID-19 infected patients can be increased. The proposed method is compared with first-come first-serve (FCFS)-based method to analyze the practical problems that arise during the assignment of hospital beds and emergency beds for the treatment of COVID-19 patients. The simulation of this work is carried out using MATLAB R2015b.
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Metadata
Title
A fuzzy rule-based efficient hospital bed management approach for coronavirus disease-19 infected patients
Authors
Kalyan Kumar Jena
Sourav Kumar Bhoi
Mukesh Prasad
Deepak Puthal
Publication date
27-01-2021
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 14/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-05719-y

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