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...
- Autores:
- 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
Groups
- Rights
- License
- Restringido (Acceso a grupos específicos)
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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 |
_version_ |
1814167714842804224 |