Stabilizing high-dimensional prediction models using feature graphs

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.