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Conference Paper: Machine learning of patient similarity: a case study on predicting survival in cancer patient after locoregional chemotherapy

TitleMachine learning of patient similarity: a case study on predicting survival in cancer patient after locoregional chemotherapy
Authors
KeywordsMachine learning
Patient similarity
Support vector machine
Cancer
Survival
Issue Date2010
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001585
Citation
The 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW 2010), Hong Kong, China, 18-21 December 2010. In Conference Proceedings, 2010, p. 467-470 How to Cite?
AbstractIdentifying historical records of patients who are similar to the new patient could help to retrieve similar reference cases for predicting the clinical outcome of the new patient. Amongst different potential applications, this study illustrates use of patient similarity in predicting survival of patients suffering from hepatocellular carcinoma (HCC) treated with locoregional chemotherapy. This study used 14 similarity measures derived from relevant clinical and imaging parameters to classify the HCC patient pairs into two classes, namely the difference between their survival time being longer or no longer than 12 months. Furthermore, this paper proposes and presents a patient similarity algorithm for the classification, named SimSVM. With the 14 similarity measures as input, SimSVM outputs the predicted class and the degree of similarity or dissimilarity. A dataset was collected from 30 patients, forming 300 and 135 patient pairs as training and test datasets respectively. The trained SimSVM with linear kernel gave the best accuracy (66.7%), sensitivity (64.8%) and specificity (67.9%) on the test dataset. ©2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/197308
ISBN

 

DC FieldValueLanguage
dc.contributor.authorChan, LWCen_US
dc.contributor.authorChan, Ten_US
dc.contributor.authorCheng, LFen_US
dc.contributor.authorMak, WSen_US
dc.date.accessioned2014-05-23T02:39:00Z-
dc.date.available2014-05-23T02:39:00Z-
dc.date.issued2010en_US
dc.identifier.citationThe 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW 2010), Hong Kong, China, 18-21 December 2010. In Conference Proceedings, 2010, p. 467-470en_US
dc.identifier.isbn978-1-4244-8302-0-
dc.identifier.urihttp://hdl.handle.net/10722/197308-
dc.description.abstractIdentifying historical records of patients who are similar to the new patient could help to retrieve similar reference cases for predicting the clinical outcome of the new patient. Amongst different potential applications, this study illustrates use of patient similarity in predicting survival of patients suffering from hepatocellular carcinoma (HCC) treated with locoregional chemotherapy. This study used 14 similarity measures derived from relevant clinical and imaging parameters to classify the HCC patient pairs into two classes, namely the difference between their survival time being longer or no longer than 12 months. Furthermore, this paper proposes and presents a patient similarity algorithm for the classification, named SimSVM. With the 14 similarity measures as input, SimSVM outputs the predicted class and the degree of similarity or dissimilarity. A dataset was collected from 30 patients, forming 300 and 135 patient pairs as training and test datasets respectively. The trained SimSVM with linear kernel gave the best accuracy (66.7%), sensitivity (64.8%) and specificity (67.9%) on the test dataset. ©2010 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001585-
dc.relation.ispartofIEEE International Conference on Bioinformatics & Biomedicine Workshops Proceedingsen_US
dc.rightsIEEE International Conference on Bioinformatics & Biomedicine Workshops. Copyright © IEEE.-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectMachine learning-
dc.subjectPatient similarity-
dc.subjectSupport vector machine-
dc.subjectCancer-
dc.subjectSurvival-
dc.titleMachine learning of patient similarity: a case study on predicting survival in cancer patient after locoregional chemotherapyen_US
dc.typeConference_Paperen_US
dc.identifier.emailChan, T: taochan@hku.hken_US
dc.identifier.authorityChan, T=rp00289en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/BIBMW.2010.5703846-
dc.identifier.scopuseid_2-s2.0-79952019435-
dc.identifier.hkuros183748en_US
dc.identifier.spage467-
dc.identifier.epage470-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 140523-

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