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Stabilizing Sparse Cox Model Using Statistic and Semantic Structures in Electronic Medical Records

  • Shivapratap GopakumarEmail author
  • Tu Dinh Nguyen
  • Truyen Tran
  • Dinh Phung
  • Svetha Venkatesh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9078)

Abstract

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.

Keywords

Electronic Medical Record Consistency Index Transfer Learning Jaccard Index Semantic Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shivapratap Gopakumar
    • 1
    Email author
  • Tu Dinh Nguyen
    • 1
  • Truyen Tran
    • 1
  • Dinh Phung
    • 1
  • Svetha Venkatesh
    • 1
  1. 1.Center for Pattern Recognition and Data AnalyticsDeakin UniversityMelbourneAustralia

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