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Article: A probabilistic SVM based decision system for pain diagnosis

TitleA probabilistic SVM based decision system for pain diagnosis
Authors
KeywordsClinical experience
Clinical treatments
Decision rules
Expert knowledge
Low-back pain
Issue Date2011
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/eswa
Citation
Expert Systems With Applications, 2011, v. 38 n. 8, p. 9346-9351 How to Cite?
AbstractLow back pain (LBP) affects a large proportion of the population and is the main cause of work disabilities worldwide. The mechanism of LBP remains largely unknown and many existing clinical treatment of LBP may be not effective to individual patients. Thus the diagnosis and treatment evaluation is crucial for LBP patients. Probabilistic support vector machine (PSVM) decision system is proposed in this article to deal with the diagnosis and treatment evaluation of LBP. The decision system consists of qualitative knowledge model and quantitative model. Expert knowledge and clinical experience are integrated into the design. To deal with the uncertainties in patients samples, PSVM is employed to learn the decision rules from data. The proposed decision system is applied to LBP patients and achieves better performance than the original system. © 2011 Published by Elsevier Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/135311
ISSN
2015 Impact Factor: 2.981
2015 SCImago Journal Rankings: 1.839
ISI Accession Number ID
Funding AgencyGrant Number
City University of Hong Kong7008057
RGC of Hong KongGRF 712408E
S.K. Yee Medical Foundation207210
Funding Information:

Authors would like to thank Dr. Xinjiang Lu, Dr. Xiaogang Duan, and Mr. Hongtao Liu for their valuable discussions. The project is partially supported by a SRG grant from City University of Hong Kong (7008057), a GRF grant from RGC of Hong Kong (GRF 712408E) and S.K. Yee Medical Foundation (207210).

Grants

 

DC FieldValueLanguage
dc.contributor.authorYang, Jen_US
dc.contributor.authorLi, HXen_US
dc.contributor.authorHu, Yen_US
dc.date.accessioned2011-07-27T01:33:11Z-
dc.date.available2011-07-27T01:33:11Z-
dc.date.issued2011en_US
dc.identifier.citationExpert Systems With Applications, 2011, v. 38 n. 8, p. 9346-9351en_US
dc.identifier.issn0957-4174-
dc.identifier.urihttp://hdl.handle.net/10722/135311-
dc.description.abstractLow back pain (LBP) affects a large proportion of the population and is the main cause of work disabilities worldwide. The mechanism of LBP remains largely unknown and many existing clinical treatment of LBP may be not effective to individual patients. Thus the diagnosis and treatment evaluation is crucial for LBP patients. Probabilistic support vector machine (PSVM) decision system is proposed in this article to deal with the diagnosis and treatment evaluation of LBP. The decision system consists of qualitative knowledge model and quantitative model. Expert knowledge and clinical experience are integrated into the design. To deal with the uncertainties in patients samples, PSVM is employed to learn the decision rules from data. The proposed decision system is applied to LBP patients and achieves better performance than the original system. © 2011 Published by Elsevier Ltd.-
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/eswa-
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.subjectClinical experience-
dc.subjectClinical treatments-
dc.subjectDecision rules-
dc.subjectExpert knowledge-
dc.subjectLow-back pain-
dc.titleA probabilistic SVM based decision system for pain diagnosisen_US
dc.typeArticleen_US
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0957-4174&volume=38&issue=8&spage=9346&epage=9351&date=2011&atitle=A+probabilistic+SVM+based+decision+system+for+pain+diagnosis-
dc.identifier.emailHu, Y: yhud@hku.hken_US
dc.identifier.authorityHu, Y=rp00432en_US
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.eswa.2011.01.106-
dc.identifier.scopuseid_2-s2.0-79953723443-
dc.identifier.hkuros189055en_US
dc.identifier.volume38en_US
dc.identifier.issue8-
dc.identifier.spage9346en_US
dc.identifier.epage9351en_US
dc.identifier.isiWOS:000290237500036-
dc.relation.projectBiomedical and electrophysiological guidance for low back pain rehabilitation and prevention-
dc.identifier.citeulike8844943-

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