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    Stabilizing high-dimensional prediction models using feature graphs

    Access Status
    Open access via publisher
    Authors
    Gopakumar, S.
    Tran, The Truyen
    Nguyen, T.
    Phung, D.
    Venkatesh, S.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Gopakumar, S. and Tran, T.T. and Nguyen, T. and Phung, D. and Venkatesh, S. 2015. Stabilizing high-dimensional prediction models using feature graphs. IEEE Journal of Biomedical and Health Informatics. 19 (3).
    Source Title
    IEEE Journal of Biomedical and Health Informatics
    DOI
    10.1109/JBHI.2014.2353031
    ISSN
    2168-2194
    School
    Multi-Sensor Proc & Content Analysis Institute
    URI
    http://hdl.handle.net/20.500.11937/27001
    Collection
    • Curtin Research Publications
    Abstract

    © 2014 IEEE. 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.

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