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Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare
Agent-based approaches have been known to be appropriate as systems and methods in medical administration in recent years. The increased attention to processes led to the recent growth of Business Process Management discipline, which quite exclusively adopt discrete-event modeling and simulation. Th...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395911/ https://www.ncbi.nlm.nih.gov/pubmed/32740823 http://dx.doi.org/10.1007/s10916-020-01608-4 |
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author | Sulis, Emilio Terna, Pietro Di Leva, Antonio Boella, Guido Boccuzzi, Adriana |
author_facet | Sulis, Emilio Terna, Pietro Di Leva, Antonio Boella, Guido Boccuzzi, Adriana |
author_sort | Sulis, Emilio |
collection | PubMed |
description | Agent-based approaches have been known to be appropriate as systems and methods in medical administration in recent years. The increased attention to processes led to the recent growth of Business Process Management discipline, which quite exclusively adopt discrete-event modeling and simulation. This paper proposes a medical agent-oriented decision support system to integrate the achievements from management science, agent-based modeling, and artificial intelligence. In particular, we performed a practical application concerning a hospital emergency department medical system. We adopt the widely used multi-agent programmable modeling environment NetLogo. First, we demonstrated the ability to perform a clear representation of healthcare processes where agents (i.e., patients and hospital staff) operate in a 3D environment. This model allows performing a traditional what-if scenario analysis. Second, we explore how performing intelligent management of patients by applying genetic algorithms to find the criteria for the selection process of the subjects in the admission procedure. The results are encouraging towards a more extensive application of agent-oriented methodologies in healthcare management. |
format | Online Article Text |
id | pubmed-7395911 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-73959112020-08-18 Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare Sulis, Emilio Terna, Pietro Di Leva, Antonio Boella, Guido Boccuzzi, Adriana J Med Syst Education & Training Agent-based approaches have been known to be appropriate as systems and methods in medical administration in recent years. The increased attention to processes led to the recent growth of Business Process Management discipline, which quite exclusively adopt discrete-event modeling and simulation. This paper proposes a medical agent-oriented decision support system to integrate the achievements from management science, agent-based modeling, and artificial intelligence. In particular, we performed a practical application concerning a hospital emergency department medical system. We adopt the widely used multi-agent programmable modeling environment NetLogo. First, we demonstrated the ability to perform a clear representation of healthcare processes where agents (i.e., patients and hospital staff) operate in a 3D environment. This model allows performing a traditional what-if scenario analysis. Second, we explore how performing intelligent management of patients by applying genetic algorithms to find the criteria for the selection process of the subjects in the admission procedure. The results are encouraging towards a more extensive application of agent-oriented methodologies in healthcare management. Springer US 2020-08-02 2020 /pmc/articles/PMC7395911/ /pubmed/32740823 http://dx.doi.org/10.1007/s10916-020-01608-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Education & Training Sulis, Emilio Terna, Pietro Di Leva, Antonio Boella, Guido Boccuzzi, Adriana Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare |
title | Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare |
title_full | Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare |
title_fullStr | Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare |
title_full_unstemmed | Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare |
title_short | Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare |
title_sort | agent-oriented decision support system for business processes management with genetic algorithm optimization: an application in healthcare |
topic | Education & Training |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395911/ https://www.ncbi.nlm.nih.gov/pubmed/32740823 http://dx.doi.org/10.1007/s10916-020-01608-4 |
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