Stabilizing high-dimensional prediction models using feature graphs.
journal contributionposted on 2015-05-01, 00:00 authored by Shivapratap Gopakumar, Truyen TranTruyen Tran, Tu Dinh Nguyen, Quoc-Dinh Phung, Svetha VenkateshSvetha Venkatesh
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.
JournalIEEE journal of biomedical and health informatics
Pagination1044 - 1052
Link to full text
Publication classificationC Journal article; C1 Refereed article in a scholarly journal
Copyright notice2015, IEEE
CategoriesNo categories selected
AgedElectronic Health RecordsFemaleHeart FailureHumansMaleModels, BiologicalModels, StatisticalReproducibility of ResultsRisk FactorsScience & TechnologyTechnologyLife Sciences & BiomedicineComputer Science, Information SystemsComputer Science, Interdisciplinary ApplicationsMathematical & Computational BiologyMedical InformaticsComputer ScienceBiomedical computingelectronic medical recordsstabilitypredictive modelsVARIABLE SELECTIONHEART-FAILUREREADMISSIONRISKHOSPITALIZATIONREGULARIZATIONDEATH