Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts
The purpose of this study was to determine whether the maintenance of medical equipment should be outsourced (or not). For this, we used data mining techniques called decision trees. We (1) collected 2364 maintenance works orders from 62 medical devices installed in a 900-bed hospital; (2) then we r...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/24266
- Acceso en línea:
- https://doi.org/10.1007/978-981-10-9023-3_52
https://repository.urosario.edu.co/handle/10336/24266
- Palabra clave:
- Biomedical engineering
Biomedical equipment
Decision trees
Errors
Maintenance
Medical computing
Obsolescence
Outsourcing
Trees (mathematics)
Alternating decision trees
Clinical engineering
Decision stumps
Maintenance management
Maintenance tasks
Maintenance work
Medical Devices
Medical equipment maintenance
Data mining
Clinical engineering
Data mining
Decision tree
Maintenance management
Outsourcing
- Rights
- License
- http://purl.org/coar/access_right/c_abf2
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Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contextsBiomedical engineeringBiomedical equipmentDecision treesErrorsMaintenanceMedical computingObsolescenceOutsourcingTrees (mathematics)Alternating decision treesClinical engineeringDecision stumpsMaintenance managementMaintenance tasksMaintenance workMedical DevicesMedical equipment maintenanceData miningClinical engineeringData miningDecision treeMaintenance managementOutsourcingThe purpose of this study was to determine whether the maintenance of medical equipment should be outsourced (or not). For this, we used data mining techniques called decision trees. We (1) collected 2364 maintenance works orders from 62 medical devices installed in a 900-bed hospital; (2) then we randomly selected 90% of the maintenance works orders to train 8 different decision tree schemas (J48 (pruned and unpruned), Naive Bayes tree, random tree, alternating decision tree, logistic model tree, decision stump, REP tree); (3) next, the remaining 10% of the works orders were used to test the decision tree schemas. The relative absolute error was used to evaluate what the tested decision tree schemas had learned; finally (4), we chose the decision tree schema with the lowest relative absolute error. Overall, the decision tree schemas performed well. 62.5% (5/8) of the decision tree schemas had less than 20% relative absolute error. 87.5% (7/8) of the decision tree schemas had more than 90% in the correct classification (whether to outsource maintenance tasks or not). The different tested decision tree schemas showed that the most important variables when making the decision whether to outsource maintenance tasks or not were: medical device, risk class (I, IIA, IIB, III), complexity, obsolescence, maintenance frequency, service time and outsourcing. The best decision tree schema was the logistic model tree (LMT) with 14.6628% relative absolute error and 94.7034% in the correct classification. © Springer Nature Singapore Pte Ltd. 2019.Springer Verlag20192020-05-26T00:10:53Zinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fapplication/pdfhttps://doi.org/10.1007/978-981-10-9023-3_522006https://repository.urosario.edu.co/handle/10336/24266instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURenghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85048276324&doi=10.1007%2f978-981-10-9023-3_52&partnerID=40&md5=af1b190bbe7db5e67cdd4146e29d24b4http://purl.org/coar/access_right/c_abf2Miguel-Cruz A.Aya-Parra P.A.Rodríguez-Dueñas, William R.Camelo-Ocampo A.F.Plata-Guao V.S.Correal O. H.H.Córdoba-Hernández N.P.Nuñez-Cruz A.Sarmiento-Rojas J.S.Quiroga-Torres, Daniel-Alejandrooai:repository.urosario.edu.co:10336/242662022-05-02T07:37:17Z |
dc.title.none.fl_str_mv |
Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts |
title |
Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts |
spellingShingle |
Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts Biomedical engineering Biomedical equipment Decision trees Errors Maintenance Medical computing Obsolescence Outsourcing Trees (mathematics) Alternating decision trees Clinical engineering Decision stumps Maintenance management Maintenance tasks Maintenance work Medical Devices Medical equipment maintenance Data mining Clinical engineering Data mining Decision tree Maintenance management Outsourcing |
title_short |
Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts |
title_full |
Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts |
title_fullStr |
Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts |
title_full_unstemmed |
Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts |
title_sort |
Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts |
dc.subject.none.fl_str_mv |
Biomedical engineering Biomedical equipment Decision trees Errors Maintenance Medical computing Obsolescence Outsourcing Trees (mathematics) Alternating decision trees Clinical engineering Decision stumps Maintenance management Maintenance tasks Maintenance work Medical Devices Medical equipment maintenance Data mining Clinical engineering Data mining Decision tree Maintenance management Outsourcing |
topic |
Biomedical engineering Biomedical equipment Decision trees Errors Maintenance Medical computing Obsolescence Outsourcing Trees (mathematics) Alternating decision trees Clinical engineering Decision stumps Maintenance management Maintenance tasks Maintenance work Medical Devices Medical equipment maintenance Data mining Clinical engineering Data mining Decision tree Maintenance management Outsourcing |
description |
The purpose of this study was to determine whether the maintenance of medical equipment should be outsourced (or not). For this, we used data mining techniques called decision trees. We (1) collected 2364 maintenance works orders from 62 medical devices installed in a 900-bed hospital; (2) then we randomly selected 90% of the maintenance works orders to train 8 different decision tree schemas (J48 (pruned and unpruned), Naive Bayes tree, random tree, alternating decision tree, logistic model tree, decision stump, REP tree); (3) next, the remaining 10% of the works orders were used to test the decision tree schemas. The relative absolute error was used to evaluate what the tested decision tree schemas had learned; finally (4), we chose the decision tree schema with the lowest relative absolute error. Overall, the decision tree schemas performed well. 62.5% (5/8) of the decision tree schemas had less than 20% relative absolute error. 87.5% (7/8) of the decision tree schemas had more than 90% in the correct classification (whether to outsource maintenance tasks or not). The different tested decision tree schemas showed that the most important variables when making the decision whether to outsource maintenance tasks or not were: medical device, risk class (I, IIA, IIB, III), complexity, obsolescence, maintenance frequency, service time and outsourcing. The best decision tree schema was the logistic model tree (LMT) with 14.6628% relative absolute error and 94.7034% in the correct classification. © Springer Nature Singapore Pte Ltd. 2019. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2020-05-26T00:10:53Z |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
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_c94f |
dc.identifier.none.fl_str_mv |
https://doi.org/10.1007/978-981-10-9023-3_52 2006 https://repository.urosario.edu.co/handle/10336/24266 |
url |
https://doi.org/10.1007/978-981-10-9023-3_52 https://repository.urosario.edu.co/handle/10336/24266 |
identifier_str_mv |
2006 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048276324&doi=10.1007%2f978-981-10-9023-3_52&partnerID=40&md5=af1b190bbe7db5e67cdd4146e29d24b4 |
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http://purl.org/coar/access_right/c_abf2 |
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http://purl.org/coar/access_right/c_abf2 |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer Verlag |
publisher.none.fl_str_mv |
Springer Verlag |
dc.source.none.fl_str_mv |
instname:Universidad del Rosario reponame:Repositorio Institucional EdocUR |
instname_str |
Universidad del Rosario |
institution |
Universidad del Rosario |
reponame_str |
Repositorio Institucional EdocUR |
collection |
Repositorio Institucional EdocUR |
repository.name.fl_str_mv |
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1803710427878653952 |