Clustering Patient Medical Records via Sparse Subspace Representation
The health industry is facing increasing challenge with “big data” as traditional methods fail to manage the scale and complexity. This paper examines clustering of patient records for chronic diseases to facilitate a better construction of care plans. We solve this problem under the framework of su...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
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Springer
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/46203 |
| _version_ | 1848757493311733760 |
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| author | Budhaditya, S. Phung, D. Pham, DucSon Venkatesh, S. |
| author2 | Pei, J. |
| author_facet | Pei, J. Budhaditya, S. Phung, D. Pham, DucSon Venkatesh, S. |
| author_sort | Budhaditya, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The health industry is facing increasing challenge with “big data” as traditional methods fail to manage the scale and complexity. This paper examines clustering of patient records for chronic diseases to facilitate a better construction of care plans. We solve this problem under the framework of subspace clustering. Our novel contribution lies in the exploitation of sparse representation to discover subspaces automatically and a domain-specific construction of weighting matrices for patient records. We show the new formulation is readily solved by extending existing 1 -regularized optimization algorithms. Using a cohort of both diabetes and stroke data we show that we outperform existing benchmark clustering techniques in the literature. |
| first_indexed | 2025-11-14T09:28:58Z |
| format | Conference Paper |
| id | curtin-20.500.11937-46203 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:28:58Z |
| publishDate | 2013 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-462032023-02-13T08:01:35Z Clustering Patient Medical Records via Sparse Subspace Representation Budhaditya, S. Phung, D. Pham, DucSon Venkatesh, S. Pei, J. Tseng, V.S. Cao, L. Motoda, H. Xu, G. data mining sparse subspace clustering convex optimization regularization healthcare The health industry is facing increasing challenge with “big data” as traditional methods fail to manage the scale and complexity. This paper examines clustering of patient records for chronic diseases to facilitate a better construction of care plans. We solve this problem under the framework of subspace clustering. Our novel contribution lies in the exploitation of sparse representation to discover subspaces automatically and a domain-specific construction of weighting matrices for patient records. We show the new formulation is readily solved by extending existing 1 -regularized optimization algorithms. Using a cohort of both diabetes and stroke data we show that we outperform existing benchmark clustering techniques in the literature. 2013 Conference Paper http://hdl.handle.net/20.500.11937/46203 10.1007/978-3-642-37456-2_11 Springer restricted |
| spellingShingle | data mining sparse subspace clustering convex optimization regularization healthcare Budhaditya, S. Phung, D. Pham, DucSon Venkatesh, S. Clustering Patient Medical Records via Sparse Subspace Representation |
| title | Clustering Patient Medical Records via Sparse Subspace Representation |
| title_full | Clustering Patient Medical Records via Sparse Subspace Representation |
| title_fullStr | Clustering Patient Medical Records via Sparse Subspace Representation |
| title_full_unstemmed | Clustering Patient Medical Records via Sparse Subspace Representation |
| title_short | Clustering Patient Medical Records via Sparse Subspace Representation |
| title_sort | clustering patient medical records via sparse subspace representation |
| topic | data mining sparse subspace clustering convex optimization regularization healthcare |
| url | http://hdl.handle.net/20.500.11937/46203 |