Interpretable Associations over DataCubes: Application to Hospital Managerial Decision Making.
The world concern about the costs of the health care systems has raised the importance of counting on precise and interpretable tools, that help the health care institution's managers to make decisions to optimize the use of health resources. In this paper we propose a new Classification based on As...
| Publicado en: | Studies in Health Technology & Informatics Vol. 205; pp. 131 - 136 |
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| Autores principales: | , , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=116234560&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 116234560 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2014 vid: 205 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 116234560 116234560 116234560 10.3233/978-1-61499-432-9-131 116234560 ppf: 131 ppct: 5 formats: tig: atl: Interpretable Associations over DataCubes: Application to Hospital Managerial Decision Making. aug: au: PRADOS DE REYES, Miguel MOLINA, Carlos PRADOS, Belén PEÑA YAÑEZ, Carmen affil: Department of Computer Sciences, University of Jaen, Jaen, Spain sug: subj: Decision Making Health Care Costs Classification Methods Organizations Classification ab: The world concern about the costs of the health care systems has raised the importance of counting on precise and interpretable tools, that help the health care institution's managers to make decisions to optimize the use of health resources. In this paper we propose a new Classification based on Association Rules (CAR) algorithm that improves the interpretability of the results, making it specially useful for decision making. Changing the usual way to obtain the rules we follow four goals: first to improve the interpretability of the result by obtaining rules meaningful and interpretable by themselves, secondly to reduce the complexity of the result obtaining a lower number of rules; thirdly, to obtain simpler rules, with less size in number of antecedents; and finally to avoid the usual over-fitting problem of the classification methods by obtaining a generic final result set, where specific rules for specific cases are avoided unless they are necessary. To prove the utility of our proposal we have used it in an example of decision support regarding the planning of the surgery rooms. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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