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An Artificial Immune System Approach for a Multi-compartment Queuing Model for Improving Medical Resources and Inpatient Bed Occupancy in PandemicsBELCIUG, S. , BEJINARIU, S.-I. , COSTIN, H.
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artificial intelligence, evolutionary computation, hospitals, optimization, queueing analysis
optimization(22), inspired(12), nature(9), algorithms(9), intelligence(8), artificial(7), systems(6), gorunescu(6), algorithm(6), selection(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2020-08-31
Volume 20, Issue 3, Year 2020, On page(s): 23 - 30
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.03003
Web of Science Accession Number: 000564453800003
SCOPUS ID: 85090354940
In the context of the Covid-19 pandemic the pressure that is put on the medical systems is increasing exponentially. Healthcare systems resources are in general scarce, and hence they require policies that ensure the optimal usage of beds and utilization costs. The aim of this study is to explore how artificial immune system approaches for a multi-queuing model may aid the hospital managers improve their resources. The proposed system outlines the route of Covid-19 patients in the intensive care unit (ICU), the compartmental model proposes a reasonable composition of the ICU, considering the queuing parameters, while the artificial immune system optimizes the needed resources (beds plus associated costs). The methodology was demonstrated through a simulation based on real data collected from official sources.
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 Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review, Chee, Marcel Lucas, Ong, Marcus Eng Hock, Siddiqui, Fahad Javaid, Zhang, Zhongheng, Lim, Shir Lynn, Ho, Andrew Fu Wah, Liu, Nan, International Journal of Environmental Research and Public Health, ISSN 1660-4601, Issue 9, Volume 18, 2021.
Digital Object Identifier: 10.3390/ijerph18094749 [CrossRef]
 The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19, Galetsi, Panagiota, Katsaliaki, Korina, Kumar, Sameer, Social Science & Medicine, ISSN 0277-9536, Issue , 2022.
Digital Object Identifier: 10.1016/j.socscimed.2022.114973 [CrossRef]
 Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images, ÖZDEMİR, Durmuş, ARSLAN, Naciye Nur, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, ISSN 2148-2446, 2022.
Digital Object Identifier: 10.29130/dubited.976118 [CrossRef]
 A data generator for covid-19 patients’ care requirements inside hospitals, Marin-Garcia, Juan A., Ruiz, Angel, Julien, Maheut, Garcia-Sabater, Jose P., WPOM-Working Papers on Operations Management, ISSN 1989-9068, Issue 1, Volume 12, 2021.
Digital Object Identifier: 10.4995/wpom.15332 [CrossRef]
 Optimization in the Context of COVID-19 Prediction and Control: A Literature Review, Jordan, Elizabeth, Shin, Delia E., Leekha, Surbhi, Azarm, Shapour, IEEE Access, ISSN 2169-3536, Issue , 2021.
Digital Object Identifier: 10.1109/ACCESS.2021.3113812 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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