Early Identification of Undesirable Outcomes for Transport Accident Injured Patients Using Semi-Supervised Clustering...Health Informatics Conference, August 12-14, 2019, Melbourne, Australia
Identifying those patient groups, who have unwanted outcomes, in the early stages is crucial to providing the most appropriate level of care. In this study, we intend to find distinctive patterns in health service use (HSU) of transport accident injured patients within the first week post-injury. Ai...
| Publicado en: | Studies in Health Technology & Informatics Vol. 266; pp. 1 - 7 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial protocol research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2019
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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=138928226&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138928226 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2019 vid: 266 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 138928226 138928226 138928226 10.3233/SHTI190764 138928226 ppf: 1 ppct: 6 formats: tig: atl: Early Identification of Undesirable Outcomes for Transport Accident Injured Patients Using Semi-Supervised Clustering...Health Informatics Conference, August 12-14, 2019, Melbourne, Australia aug: au: KHORSHIDI, Hadi A. HAFFARI, Gholamreza AICKELIN, Uwe HASSANI-MAHMOOEI, Behrooz affil: School of Computing & Information Systems, The University of Melbourne, Australia sug: subj: Accidents, Traffic Wounds and Injuries Health Services Utilization Health Care Costs Congresses and Conferences Victoria Victoria Human Cluster Analysis Funding Source ab: Identifying those patient groups, who have unwanted outcomes, in the early stages is crucial to providing the most appropriate level of care. In this study, we intend to find distinctive patterns in health service use (HSU) of transport accident injured patients within the first week post-injury. Aiming those patterns that are associated with the outcome of interest. To recognize these patterns, we propose a multi-objective optimization model that minimizes the k-medians cost function and regression error simultaneously. Thus, we use a semi-supervised clustering approach to identify patient groups based on HSU patterns and their association with total cost. To solve the optimization problem, we introduce an evolutionary algorithm using stochastic gradient descent and Pareto optimal solutions. As a result, we find the best optimal clusters by minimizing both objective functions. The results show that the proposed semi-supervised approach identifies distinct groups of HSUs and contributes to predict total cost. Also, the experiments prove the performance of the multi-objective approach in comparison with singleobjective approaches. pubtype: Academic Journal doctype: equations & formulas pictorial protocol research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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