Stabilizing high-dimensional prediction models using feature graphs
| dc.contributor.author | Gopakumar, S. | |
| dc.contributor.author | Tran, The Truyen | |
| dc.contributor.author | Nguyen, T. | |
| dc.contributor.author | Phung, D. | |
| dc.contributor.author | Venkatesh, S. | |
| dc.date.accessioned | 2017-01-30T12:56:24Z | |
| dc.date.available | 2017-01-30T12:56:24Z | |
| dc.date.created | 2015-10-29T04:09:59Z | |
| dc.date.issued | 2015 | |
| dc.identifier.citation | 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). | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/27001 | |
| dc.identifier.doi | 10.1109/JBHI.2014.2353031 | |
| dc.description.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. | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.title | Stabilizing high-dimensional prediction models using feature graphs | |
| dc.type | Journal Article | |
| dcterms.source.volume | 19 | |
| dcterms.source.number | 3 | |
| dcterms.source.issn | 2168-2194 | |
| dcterms.source.title | IEEE Journal of Biomedical and Health Informatics | |
| curtin.department | Multi-Sensor Proc & Content Analysis Institute | |
| curtin.accessStatus | Open access via publisher |
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