A neural-network-based model for the removal of biomedical equipment from a hospital inventory

This article puts forward an accurate and robust model based on a artificial neural network that guarantees a warning when a piece of medical equipment requires replacement. A perceptron neural network composed by one input layer with two neurons is described. The artificial neural network can class...

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Tipo de recurso:
Fecha de publicación:
2006
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/27609
Acceso en línea:
https://repository.urosario.edu.co/handle/10336/27609
Palabra clave:
Artificial neural network
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id EDOCUR2_3bd07c79b3b5ff048bd6b70d813f7ac0
oai_identifier_str oai:repository.urosario.edu.co:10336/27609
network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
spelling f2e876d6-94d1-4fd7-bc85-4194ad0925a6-1ddb090f9-b039-434d-b526-d9d445091654-12020-08-19T14:42:58Z2020-08-19T14:42:58Z2006This article puts forward an accurate and robust model based on a artificial neural network that guarantees a warning when a piece of medical equipment requires replacement. A perceptron neural network composed by one input layer with two neurons is described. The artificial neural network can classify data in groups. In this research three groups were classified. These groups depend on numerical values of service cost/acquisition cost and usage time/useful life time ratios. A supervised learning rule to train the artificial neural network was selected. The training process was carried out by collecting typical data from 200 high-performance as well as 100 low-performance devices from four hospitals under study. The network was tested by collecting data (998 high-performance as well as 765 low-performance devices) in 4 hospitals. In 100 % of the cases the artificial neural network classified the equipment in the expected groups. It can be concluded that the network had a great level of data discrimination and an excellent performance level.application/pdfISSN: 0363-8855EISSN: 1550-3275https://repository.urosario.edu.co/handle/10336/27609engWolters Kluwer Health144No. 3140Journal of Clinical EngineeringVol. 31Journal of Clinical Engineering, ISSN: 0363-8855;EISSN: 1550-3275, Vol.31, No.3 (July-September 2006); pp. 140-144https://journals.lww.com/jcejournal/Abstract/2006/07000/A_Neural_Network_Based_Model_for_the_Removal_of.21.aspxRestringido (Acceso a grupos específicos)http://purl.org/coar/access_right/c_16ecJournal of Clinical Engineeringinstname:Universidad del Rosarioreponame:Repositorio Institucional EdocURArtificial neural networkGroupsA neural-network-based model for the removal of biomedical equipment from a hospital inventoryUn modelo basado en redes neuronales para la eliminación de equipos biomédicos del inventario de un hospitalarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Cruz, Antonio MiguelRodríguez, Denis Ernesto10336/27609oai:repository.urosario.edu.co:10336/276092021-06-03 00:50:15.956https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co
dc.title.spa.fl_str_mv A neural-network-based model for the removal of biomedical equipment from a hospital inventory
dc.title.TranslatedTitle.spa.fl_str_mv Un modelo basado en redes neuronales para la eliminación de equipos biomédicos del inventario de un hospital
title A neural-network-based model for the removal of biomedical equipment from a hospital inventory
spellingShingle A neural-network-based model for the removal of biomedical equipment from a hospital inventory
Artificial neural network
Groups
title_short A neural-network-based model for the removal of biomedical equipment from a hospital inventory
title_full A neural-network-based model for the removal of biomedical equipment from a hospital inventory
title_fullStr A neural-network-based model for the removal of biomedical equipment from a hospital inventory
title_full_unstemmed A neural-network-based model for the removal of biomedical equipment from a hospital inventory
title_sort A neural-network-based model for the removal of biomedical equipment from a hospital inventory
dc.subject.keyword.spa.fl_str_mv Artificial neural network
Groups
topic Artificial neural network
Groups
description This article puts forward an accurate and robust model based on a artificial neural network that guarantees a warning when a piece of medical equipment requires replacement. A perceptron neural network composed by one input layer with two neurons is described. The artificial neural network can classify data in groups. In this research three groups were classified. These groups depend on numerical values of service cost/acquisition cost and usage time/useful life time ratios. A supervised learning rule to train the artificial neural network was selected. The training process was carried out by collecting typical data from 200 high-performance as well as 100 low-performance devices from four hospitals under study. The network was tested by collecting data (998 high-performance as well as 765 low-performance devices) in 4 hospitals. In 100 % of the cases the artificial neural network classified the equipment in the expected groups. It can be concluded that the network had a great level of data discrimination and an excellent performance level.
publishDate 2006
dc.date.created.spa.fl_str_mv 2006
dc.date.accessioned.none.fl_str_mv 2020-08-19T14:42:58Z
dc.date.available.none.fl_str_mv 2020-08-19T14:42:58Z
dc.type.eng.fl_str_mv article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.spa.spa.fl_str_mv Artículo
dc.identifier.issn.none.fl_str_mv ISSN: 0363-8855
EISSN: 1550-3275
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/27609
identifier_str_mv ISSN: 0363-8855
EISSN: 1550-3275
url https://repository.urosario.edu.co/handle/10336/27609
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationEndPage.none.fl_str_mv 144
dc.relation.citationIssue.none.fl_str_mv No. 3
dc.relation.citationStartPage.none.fl_str_mv 140
dc.relation.citationTitle.none.fl_str_mv Journal of Clinical Engineering
dc.relation.citationVolume.none.fl_str_mv Vol. 31
dc.relation.ispartof.spa.fl_str_mv Journal of Clinical Engineering, ISSN: 0363-8855;EISSN: 1550-3275, Vol.31, No.3 (July-September 2006); pp. 140-144
dc.relation.uri.spa.fl_str_mv https://journals.lww.com/jcejournal/Abstract/2006/07000/A_Neural_Network_Based_Model_for_the_Removal_of.21.aspx
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.rights.acceso.spa.fl_str_mv Restringido (Acceso a grupos específicos)
rights_invalid_str_mv Restringido (Acceso a grupos específicos)
http://purl.org/coar/access_right/c_16ec
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Wolters Kluwer Health
dc.source.spa.fl_str_mv Journal of Clinical Engineering
institution Universidad del Rosario
dc.source.instname.none.fl_str_mv instname:Universidad del Rosario
dc.source.reponame.none.fl_str_mv reponame:Repositorio Institucional EdocUR
repository.name.fl_str_mv Repositorio institucional EdocUR
repository.mail.fl_str_mv edocur@urosario.edu.co
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