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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.

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Title Stabilizing sparse cox model using statistic and semantic structures in electronic medical records
Author(s) Gopakumar, Shivapratap
Nguyen, Tu Dinh
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for 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
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