title: Stabilizing high-dimensional prediction models using feature graphs.
abstract: 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.
authors.forename: Shivapratap
authors.initials: S
authors.lastname: Gopakumar
authors.forename: Truyen
authors.initials: T
authors.lastname: Tran
authors.forename: Tu Dinh
authors.initials: TD
authors.lastname: Nguyen
authors.forename: Dinh
authors.initials: D
authors.lastname: Phung
authors.forename: Svetha
authors.initials: S
authors.lastname: Venkatesh
mesh_terms: D000368:Aged; D057286:Electronic Health Records; D005260:Female; D006333:Heart Failure; D006801:Humans; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D015203:Reproducibility of Results; D012307:Risk Factors