Stabilized sparse ordinal regression for medical risk stratification
The recent wide adoption of electronic medical records (EMRs) presents great opportunities and challenges for data mining. The EMR data are largely temporal, often noisy, irregular and high dimensional. This paper constructs a novel ordinal regression framework for predicting medical risk stratifica...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/17743 |
| _version_ | 1848749545281814528 |
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| author | Tran, The Truyen Phung, D. Luo, W. Venkatesh, S. |
| author_facet | Tran, The Truyen Phung, D. Luo, W. Venkatesh, S. |
| author_sort | Tran, The Truyen |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The recent wide adoption of electronic medical records (EMRs) presents great opportunities and challenges for data mining. The EMR data are largely temporal, often noisy, irregular and high dimensional. This paper constructs a novel ordinal regression framework for predicting medical risk stratification from EMR. First, a conceptual view of EMR as a temporal image is constructed to extract a diverse set of features. Second, ordinal modeling is applied for predicting cumulative or progressive risk. The challenges are building a transparent predictive model that works with a large number of weakly predictive features, and at the same time, is stable against resampling variations. Our solution employs sparsity methods that are stabilized through domain-specific feature interaction networks. We introduces two indices that measure the model stability against data resampling. Feature networks are used to generate two multivariate Gaussian priors with sparse precision matrices (the Laplacian and Random Walk). We apply the framework on a large short-term suicide risk prediction problem and demonstrate that our methods outperform clinicians to a large margin, discover suicide risk factors that conform with mental health knowledge, and produce models with enhanced stability. © 2014 Springer-Verlag London. |
| first_indexed | 2025-11-14T07:22:38Z |
| format | Journal Article |
| id | curtin-20.500.11937-17743 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:22:38Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-177432018-03-29T09:06:21Z Stabilized sparse ordinal regression for medical risk stratification Tran, The Truyen Phung, D. Luo, W. Venkatesh, S. The recent wide adoption of electronic medical records (EMRs) presents great opportunities and challenges for data mining. The EMR data are largely temporal, often noisy, irregular and high dimensional. This paper constructs a novel ordinal regression framework for predicting medical risk stratification from EMR. First, a conceptual view of EMR as a temporal image is constructed to extract a diverse set of features. Second, ordinal modeling is applied for predicting cumulative or progressive risk. The challenges are building a transparent predictive model that works with a large number of weakly predictive features, and at the same time, is stable against resampling variations. Our solution employs sparsity methods that are stabilized through domain-specific feature interaction networks. We introduces two indices that measure the model stability against data resampling. Feature networks are used to generate two multivariate Gaussian priors with sparse precision matrices (the Laplacian and Random Walk). We apply the framework on a large short-term suicide risk prediction problem and demonstrate that our methods outperform clinicians to a large margin, discover suicide risk factors that conform with mental health knowledge, and produce models with enhanced stability. © 2014 Springer-Verlag London. 2014 Journal Article http://hdl.handle.net/20.500.11937/17743 10.1007/s10115-014-0740-4 restricted |
| spellingShingle | Tran, The Truyen Phung, D. Luo, W. Venkatesh, S. Stabilized sparse ordinal regression for medical risk stratification |
| title | Stabilized sparse ordinal regression for medical risk stratification |
| title_full | Stabilized sparse ordinal regression for medical risk stratification |
| title_fullStr | Stabilized sparse ordinal regression for medical risk stratification |
| title_full_unstemmed | Stabilized sparse ordinal regression for medical risk stratification |
| title_short | Stabilized sparse ordinal regression for medical risk stratification |
| title_sort | stabilized sparse ordinal regression for medical risk stratification |
| url | http://hdl.handle.net/20.500.11937/17743 |