Publication Type : Journal Article
Source : IEEE Journal of Biomedical and Health Informatics 19.3 (2015). DOI: 10.1109/JBHI.2014.2353031
Url : https://ieeexplore.ieee.org/document/6887285
Campus : Amritapuri
School : School for Sustainable Futures
Year : 2015
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.
Cite this Research Publication : S. Gopakumar, T. Tran, T. D. Nguyen, D. Phung, and S. Venkatesh, "Stabilizing High-Dimensional Prediction Models Using Feature Graphs," IEEE Journal of Biomedical and Health Informatics 19.3 (2015). DOI: 10.1109/JBHI.2014.2353031