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...
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| Formato: | Objeto de conferencia (Conference Object) |
| Lenguaje: | Inglés (English) |
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Springer Verlag
2019
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| Acceso en línea: | https://repository.urosario.edu.co/handle/10336/24266 https://doi.org/10.1007/978-981-10-9023-3_52 |
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ir-10336-242662022-05-02T12:37:17Z Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts Miguel-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-Alejandro 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 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. 2019 2020-05-26T00:10:53Z info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion 2006 https://repository.urosario.edu.co/handle/10336/24266 https://doi.org/10.1007/978-981-10-9023-3_52 eng info:eu-repo/semantics/openAccess application/pdf Springer Verlag instname:Universidad del Rosario |
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EdocUR - Universidad del Rosario |
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DSpace |
| language |
Inglés (English) |
| 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 |
| spellingShingle |
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 Miguel-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-Alejandro Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts |
| 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. |
| format |
Objeto de conferencia (Conference Object) |
| author |
Miguel-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-Alejandro |
| author_facet |
Miguel-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-Alejandro |
| author_sort |
Miguel-Cruz A. |
| title |
Using data mining techniques to determine whether to outsource medical equipment maintenance tasks in real contexts |
| 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 |
| publisher |
Springer Verlag |
| publishDate |
2019 |
| url |
https://repository.urosario.edu.co/handle/10336/24266 https://doi.org/10.1007/978-981-10-9023-3_52 |
| _version_ |
1740172270853160960 |
| score |
12,131701 |