Stabilizing high-dimensional prediction models using feature graphs
© 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...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/27001 |
| _version_ | 1848752142764998656 |
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| author | Gopakumar, S. Tran, The Truyen Nguyen, T. Phung, D. Venkatesh, S. |
| author_facet | Gopakumar, S. Tran, The Truyen Nguyen, T. Phung, D. Venkatesh, S. |
| author_sort | Gopakumar, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T08:03:55Z |
| format | Journal Article |
| id | curtin-20.500.11937-27001 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:03:55Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-270012017-09-13T15:32:01Z Stabilizing high-dimensional prediction models using feature graphs Gopakumar, S. Tran, The Truyen Nguyen, T. Phung, D. Venkatesh, S. © 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. 2015 Journal Article http://hdl.handle.net/20.500.11937/27001 10.1109/JBHI.2014.2353031 Institute of Electrical and Electronics Engineers Inc. unknown |
| spellingShingle | Gopakumar, S. Tran, The Truyen Nguyen, T. Phung, D. Venkatesh, S. Stabilizing high-dimensional prediction models using feature graphs |
| title | Stabilizing high-dimensional prediction models using feature graphs |
| title_full | Stabilizing high-dimensional prediction models using feature graphs |
| title_fullStr | Stabilizing high-dimensional prediction models using feature graphs |
| title_full_unstemmed | Stabilizing high-dimensional prediction models using feature graphs |
| title_short | Stabilizing high-dimensional prediction models using feature graphs |
| title_sort | stabilizing high-dimensional prediction models using feature graphs |
| url | http://hdl.handle.net/20.500.11937/27001 |