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.
In REF , the feature stability is observed by using the regression based feature graph for the clinical prognosis in high-dimensional electronic medical records.
Stabilizing High-Dimensional Prediction Models Using Feature Graphs
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics
Computer Science Medicine