Stabilizing sparse Cox model using statistic and semantic structures in electronic medical records
Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data, which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/9928 |
| _version_ | 1848746091239964672 |
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| author | Gopakumar, S. Nguyen, T. Tran, The Truyen Phung, D. Venkatesh, S. |
| author_facet | Gopakumar, S. Nguyen, T. Tran, The Truyen Phung, D. Venkatesh, S. |
| author_sort | Gopakumar, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data, which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events using statistical and semantic structures inherent in Electronic Medical Records (EMR). Model estimation is stabilized using three feature graphs built from (i) Jaccard similarity among features (ii) aggregation of Jaccard similarity graph and a recently introduced semantic EMR graph (iii) Jaccard similarity among features transferred from a related cohort. Our experiments are conducted on two real world hospital datasets: a heart failure cohort and a diabetes cohort. On two stability measures - the Consistency index and signal-to-noise ratio (SNR) - the use of our proposed methods significantly increased feature stability when compared with the baselines. |
| first_indexed | 2025-11-14T06:27:44Z |
| format | Conference Paper |
| id | curtin-20.500.11937-9928 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:27:44Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-99282018-03-29T09:05:56Z Stabilizing sparse Cox model using statistic and semantic structures in electronic medical records Gopakumar, S. Nguyen, T. Tran, The Truyen Phung, D. Venkatesh, S. Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data, which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events using statistical and semantic structures inherent in Electronic Medical Records (EMR). Model estimation is stabilized using three feature graphs built from (i) Jaccard similarity among features (ii) aggregation of Jaccard similarity graph and a recently introduced semantic EMR graph (iii) Jaccard similarity among features transferred from a related cohort. Our experiments are conducted on two real world hospital datasets: a heart failure cohort and a diabetes cohort. On two stability measures - the Consistency index and signal-to-noise ratio (SNR) - the use of our proposed methods significantly increased feature stability when compared with the baselines. 2015 Conference Paper http://hdl.handle.net/20.500.11937/9928 10.1007/978-3-319-18032-8_26 restricted |
| spellingShingle | Gopakumar, S. Nguyen, T. Tran, The Truyen Phung, D. Venkatesh, S. Stabilizing sparse Cox model using statistic and semantic structures in electronic medical records |
| title | Stabilizing sparse Cox model using statistic and semantic structures in electronic medical records |
| title_full | Stabilizing sparse Cox model using statistic and semantic structures in electronic medical records |
| title_fullStr | Stabilizing sparse Cox model using statistic and semantic structures in electronic medical records |
| title_full_unstemmed | Stabilizing sparse Cox model using statistic and semantic structures in electronic medical records |
| title_short | Stabilizing sparse Cox model using statistic and semantic structures in electronic medical records |
| title_sort | stabilizing sparse cox model using statistic and semantic structures in electronic medical records |
| url | http://hdl.handle.net/20.500.11937/9928 |