Mining the preferences of patients for ubiquitous clinic recommendation.
A challenge facing all ubiquitous clinic recommendation systems is that patients often have difficulty articulating their requirements. To overcome this problem, a ubiquitous clinic recommendation mechanism was designed in this study by mining the clinic preferences of patients. Their preferences we...
| Publicado en: | Health Care Management Science Vol. 23; no. 2; pp. 173 - 185 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
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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=143699618&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143699618 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13869620 BSE jtl: Health Care Management Science issn: 13869620 maglogo: N pubinfo: dt: Jun2020 vid: 23 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143699618 143699618 NLM29511976 143699618 10.1007/s10729-018-9441-y NLM29511976 143699618 ppf: 173 ppct: 12 formats: tig: atl: Mining the preferences of patients for ubiquitous clinic recommendation. aug: au: Chen, Tin-Chih Toly Chiu, Min-Chi affil: Department of Industrial Engineering and Management, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan sug: subj: Ambulatory Care Facilities Administration Patient Satisfaction Appointments and Schedules Time Factors Data Mining Methods Chaos Theory Taiwan Human ab: A challenge facing all ubiquitous clinic recommendation systems is that patients often have difficulty articulating their requirements. To overcome this problem, a ubiquitous clinic recommendation mechanism was designed in this study by mining the clinic preferences of patients. Their preferences were defined using the weights in the ubiquitous clinic recommendation mechanism. An integer nonlinear programming problem was solved to tune the values of the weights on a rolling basis. In addition, since it may take a long time to adjust the values of weights to their asymptotic values, the back propagation network (BPN)-response surface method (RSM) method is applied to estimate the asymptotic values of weights. The proposed methodology was tested in a regional study. Experimental results indicated that the ubiquitous clinic recommendation system outperformed several existing methods in improving the successful recommendation rate. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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