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Stabilizing sparse cox model using statistic and semantic structures in electronic medical records
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posted on 2015-01-01, 00:00 authored by Shivapratap Gopakumar, Tu Dinh Nguyen, Truyen TranTruyen Tran, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshStability 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.
History
Event
Pacific-Asia Conference on Knowledge Discovery and Data MiningTitle 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 IIVolume
9078Series
Lecture notes in computer science; v.9078Chapter number
26Pagination
331 - 343Publisher
SpringerLocation
VietnamPlace of publication
Berlin, GermanyPublisher DOI
Start date
2015-01-01End date
2015-01-01ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319180328Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2015, IEEEExtent
59Editor/Contributor(s)
T Cao, E Lim, Z Zhou, T Ho, D Cheung, H MotodaUsage metrics
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