Stabilizing sparse cox model using statistic and semantic structures in electronic medical records Gopakumar, Shivapratap, Nguyen, Tu Dinh, Tran, Truyen, Phung, Dinh and Venkatesh, Svetha 2015, Stabilizing sparse cox model using statistic and semantic structures in electronic medical records. In Cao, Tru, Lim, Ee-Peng, Zhou, Zhi-Hua, Ho, Tu-Bao, Cheung, David and Motoda, Hiroshi (ed), Advances in knowledge discovery and data mining 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II, Springer, Berlin, Germany, pp.331-343, doi: 10.1007/978-3-319-18032-8_26. Attached Files Name Description MIMEType Size Downloads Title Stabilizing sparse cox model using statistic and semantic structures in electronic medical records Author(s) Gopakumar, Shivapratap Nguyen, Tu Dinh Tran, Truyen orcid.org/0000-0001-6531-8907 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Title of book Advances in knowledge discovery and data mining 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II Editor(s) Cao, Tru Lim, Ee-Peng Zhou, Zhi-Hua Ho, Tu-Bao Cheung, David Motoda, Hiroshi Publication date 2015 Series Lecture notes in computer science; v.9078 Chapter number 26 Total chapters 59 Start page 331 End page 343 Total pages 13 Publisher Springer Place of Publication Berlin, Germany Keyword(s) Science & Technology Technology Computer Science, Artificial Intelligence Computer Science, Information Systems Computer Science, Theory & Methods Computer Science SIGNATURE DISCOVERY VARIABLE SELECTION NETWORK KNOWLEDGE REGRESSION LASSO Summary Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data, which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events using statistical and semantic structures inherent in Electronic Medical Records (EMR). Model estimation is stabilized using three feature graphs built from (i) Jaccard similarity among features (ii) aggregation of Jaccard similarity graph and a recently introduced semantic EMR graph (iii) Jaccard similarity among features transferred from a related cohort. Our experiments are conducted on two real world hospital datasets: a heart failure cohort and a diabetes cohort. On two stability measures – the Consistency index and signal-to-noise ratio (SNR) – the use of our proposed methods significantly increased feature stability when compared with the baselines. ISBN 9783319180328 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-18032-8_26 Field of Research 080109 Pattern Recognition and Data Mining 08 Information And Computing Sciences Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category B1 Book chapter ERA Research output type B Book chapter Copyright notice ©2015, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30076887 Document type: Book Chapter Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 26 Aug 2015, 14:50:04 EST Thu, 27 Aug 2015, 05:08:34 EST Thu, 27 Aug 2015, 08:51:51 EST Thu, 27 Aug 2015, 08:58:05 EST Thu, 03 Mar 2016, 15:22:58 EST Thu, 07 Apr 2016, 16:41:04 EST Fri, 08 Apr 2016, 06:07:10 EST Tue, 12 Apr 2016, 09:22:54 EST Tue, 12 Apr 2016, 09:38:50 EST Tue, 13 Mar 2018, 08:31:10 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 329 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Thu, 07 Apr 2016, 16:41:04 EST

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