Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine
Efficient management of chronic diseases is critical in modern health care. We consider diabetes mellitus, and our ongoing goal is to examine how machine learning can deliver information for clinical efficiency. The challenge is to aggregate highly heterogeneous sources including demographics, diagn...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/12693 |
| _version_ | 1848748148185366528 |
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| author | Nguyen, T. Tran, The Truyen Phung, D. Venkatesh, S. |
| author_facet | Nguyen, T. Tran, The Truyen Phung, D. Venkatesh, S. |
| author_sort | Nguyen, T. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Efficient management of chronic diseases is critical in modern health care. We consider diabetes mellitus, and our ongoing goal is to examine how machine learning can deliver information for clinical efficiency. The challenge is to aggregate highly heterogeneous sources including demographics, diagnoses, pathologies and treatments, and extract similar groups so that care plans can be designed. To this end, we extend our recent model, the mixed-variate restricted Boltzmann machine (MV.RBM), as it seamlessly integrates multiple data types for each patient aggregated over time and outputs a homogeneous representation called "latent profile" that can be used for patient clustering, visualisation, disease correlation analysis and prediction. We demonstrate that the method outperforms all baselines on these tasks - the primary characteristics of patients in the same groups are able to be identified and the good result can be achieved for the diagnosis codes prediction. © Springer-Verlag 2013. |
| first_indexed | 2025-11-14T07:00:26Z |
| format | Conference Paper |
| id | curtin-20.500.11937-12693 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:00:26Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-126932017-09-13T14:59:52Z Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine Nguyen, T. Tran, The Truyen Phung, D. Venkatesh, S. Efficient management of chronic diseases is critical in modern health care. We consider diabetes mellitus, and our ongoing goal is to examine how machine learning can deliver information for clinical efficiency. The challenge is to aggregate highly heterogeneous sources including demographics, diagnoses, pathologies and treatments, and extract similar groups so that care plans can be designed. To this end, we extend our recent model, the mixed-variate restricted Boltzmann machine (MV.RBM), as it seamlessly integrates multiple data types for each patient aggregated over time and outputs a homogeneous representation called "latent profile" that can be used for patient clustering, visualisation, disease correlation analysis and prediction. We demonstrate that the method outperforms all baselines on these tasks - the primary characteristics of patients in the same groups are able to be identified and the good result can be achieved for the diagnosis codes prediction. © Springer-Verlag 2013. 2013 Conference Paper http://hdl.handle.net/20.500.11937/12693 10.1007/978-3-642-37453-1_11 restricted |
| spellingShingle | Nguyen, T. Tran, The Truyen Phung, D. Venkatesh, S. Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine |
| title | Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine |
| title_full | Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine |
| title_fullStr | Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine |
| title_full_unstemmed | Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine |
| title_short | Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine |
| title_sort | latent patient profile modelling and applications with mixed-variate restricted boltzmann machine |
| url | http://hdl.handle.net/20.500.11937/12693 |