Increasing the Efficiency of Using CCU Beds of Hospitals through Optimization and Combination of Genetic Algorithm and Imperialist Competitive
Introduction: Hospitals as the most important consumer of resources in healthcare section are highly sensitive to maximum consumption of minimum existing resources. In the recent two decades, computer data processing to extract knowledge and improve utilization of resources has attracted the attenti...
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| Format: | Article |
| Language: | fas |
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Kerman University of Medical Sciences
2018-03-01
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| Series: | مجله انفورماتیک سلامت و زیست پزشکی |
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| Online Access: | http://jhbmi.ir/article-1-258-en.html |
| _version_ | 1828052175851356160 |
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| author | Asma Taghavi Hosein Monem |
| author_facet | Asma Taghavi Hosein Monem |
| author_sort | Asma Taghavi |
| collection | DOAJ |
| description | Introduction: Hospitals as the most important consumer of resources in healthcare section are highly sensitive to maximum consumption of minimum existing resources. In the recent two decades, computer data processing to extract knowledge and improve utilization of resources has attracted the attention of organizations. In this study, allocation of CCU beds in Shahid Faghihi Hospital of Shiraz is investigated through optimizing and combining Genetic Algorithm (GA) and Imperialist Competitive Algorithm (ICA).
Methods: In this analytic cross-sectional study, patients were monitored through optimization and combination of genetic algorithm and imperialist competitive and allocated to CCU beds in the spring of 2016. To this end, number of patients, number of beds, number of doctors and vital signs of patients were used as input and configuration of patients and allocation optimization were considered as output. MATLAB 2012 was used to analyze data.
Results: Results of this study show that ICA is more efficient compared to GA in optimization of allocating CCU beds to patients. Moreover, the hybrid algorithm obtained from combination of ICA and GA is more efficient than ICA.
Conclusion: In the process of this study, priorities of patients' hospitalization and also manner of hospitalization were determined and suggestions for allocation of beds to patients were presented. |
| first_indexed | 2024-04-10T19:49:33Z |
| format | Article |
| id | doaj.art-72872cc1297847b9ba307b55c73f7c1b |
| institution | Directory Open Access Journal |
| issn | 2423-3870 2423-3498 |
| language | fas |
| last_indexed | 2024-04-10T19:49:33Z |
| publishDate | 2018-03-01 |
| publisher | Kerman University of Medical Sciences |
| record_format | Article |
| series | مجله انفورماتیک سلامت و زیست پزشکی |
| spelling | doaj.art-72872cc1297847b9ba307b55c73f7c1b2023-01-28T10:41:57ZfasKerman University of Medical Sciencesمجله انفورماتیک سلامت و زیست پزشکی2423-38702423-34982018-03-0144253265Increasing the Efficiency of Using CCU Beds of Hospitals through Optimization and Combination of Genetic Algorithm and Imperialist CompetitiveAsma Taghavi0Hosein Monem1 Ph.D. in Information Systems, Assistant Professor, Information Technology Dept., School of Paramedicine, Shiraz University of Medical Sciences, Shiraz, Iran Introduction: Hospitals as the most important consumer of resources in healthcare section are highly sensitive to maximum consumption of minimum existing resources. In the recent two decades, computer data processing to extract knowledge and improve utilization of resources has attracted the attention of organizations. In this study, allocation of CCU beds in Shahid Faghihi Hospital of Shiraz is investigated through optimizing and combining Genetic Algorithm (GA) and Imperialist Competitive Algorithm (ICA). Methods: In this analytic cross-sectional study, patients were monitored through optimization and combination of genetic algorithm and imperialist competitive and allocated to CCU beds in the spring of 2016. To this end, number of patients, number of beds, number of doctors and vital signs of patients were used as input and configuration of patients and allocation optimization were considered as output. MATLAB 2012 was used to analyze data. Results: Results of this study show that ICA is more efficient compared to GA in optimization of allocating CCU beds to patients. Moreover, the hybrid algorithm obtained from combination of ICA and GA is more efficient than ICA. Conclusion: In the process of this study, priorities of patients' hospitalization and also manner of hospitalization were determined and suggestions for allocation of beds to patients were presented.http://jhbmi.ir/article-1-258-en.htmloptimizationresource allocationgenetic algorithmsimperialist competitive a algorithmshybrid algorithmccu |
| spellingShingle | Asma Taghavi Hosein Monem Increasing the Efficiency of Using CCU Beds of Hospitals through Optimization and Combination of Genetic Algorithm and Imperialist Competitive مجله انفورماتیک سلامت و زیست پزشکی optimization resource allocation genetic algorithms imperialist competitive a algorithms hybrid algorithm ccu |
| title | Increasing the Efficiency of Using CCU Beds of Hospitals through Optimization and Combination of Genetic Algorithm and Imperialist Competitive |
| title_full | Increasing the Efficiency of Using CCU Beds of Hospitals through Optimization and Combination of Genetic Algorithm and Imperialist Competitive |
| title_fullStr | Increasing the Efficiency of Using CCU Beds of Hospitals through Optimization and Combination of Genetic Algorithm and Imperialist Competitive |
| title_full_unstemmed | Increasing the Efficiency of Using CCU Beds of Hospitals through Optimization and Combination of Genetic Algorithm and Imperialist Competitive |
| title_short | Increasing the Efficiency of Using CCU Beds of Hospitals through Optimization and Combination of Genetic Algorithm and Imperialist Competitive |
| title_sort | increasing the efficiency of using ccu beds of hospitals through optimization and combination of genetic algorithm and imperialist competitive |
| topic | optimization resource allocation genetic algorithms imperialist competitive a algorithms hybrid algorithm ccu |
| url | http://jhbmi.ir/article-1-258-en.html |
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