Stabilizing high-dimensional prediction models using feature graphs

Gopakumar, Shivapratap, Tran, Truyen, Nguyen, Tu Dinh, Phung, Dinh and Venkatesh, Svetha 2015, Stabilizing high-dimensional prediction models using feature graphs, IEEE journal of biomedical and health informatics, vol. 19, no. 3, pp. 1044-1052, doi: 10.1109/JBHI.2014.2353031.

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Title Stabilizing high-dimensional prediction models using feature graphs
Author(s) Gopakumar, Shivapratap
Tran, TruyenORCID iD for Tran, Truyen
Nguyen, Tu Dinh
Phung, DinhORCID iD for Phung, Dinh
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Journal name IEEE journal of biomedical and health informatics
Volume number 19
Issue number 3
Start page 1044
End page 1052
Total pages 9
Publisher IEEE
Place of publication Champaign, III.
Publication date 2015-05
ISSN 2168-2208
Keyword(s) Aged
Electric Health Records
Heart Failure
Models, Biological
Models, Statistical
Reproductility of Results
Risk Factors
Summary We investigate feature stability in the context of clinical prognosis derived from high-dimensional electronic medical records. To reduce variance in the selected features that are predictive, we introduce Laplacian-based regularization into a regression model. The Laplacian is derived on a feature graph that captures both the temporal and hierarchic relations between hospital events, diseases, and interventions. Using a cohort of patients with heart failure, we demonstrate better feature stability and goodness-of-fit through feature graph stabilization.
Language eng
DOI 10.1109/JBHI.2014.2353031
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2015, IEEE
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Document type: Journal Article
Collections: Centre for Pattern Recognition and Data Analytics
2018 ERA Submission
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