Clustering patient medical records via sparse subspace representation

Budhaditya Saha; Duc Son Pham; Dinh Phung; Svetha Venkatesh

doi:10.1007/978-3-642-37456-2_11

Clustering patient medical records via sparse subspace representation

Budhaditya Saha, Duc Son Pham, Dinh Phung, Svetha Venkatesh

Department of Data Science & AI

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

4 Citations (Scopus)

Abstract

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 ℓ₁ -regularized optimization algorithms. Using a cohort of both diabetes and stroke data we show that we outperform existing benchmark clustering techniques in the literature.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
Pages	123-134
Number of pages	12
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-37456-2_11
Publication status	Published - 1 Dec 2013
Event	Pacific-Asia Conference on Knowledge Discovery and Data Mining 2013 - Gold Coast, Australia Duration: 14 Apr 2013 → 17 Apr 2013 Conference number: 17th https://link.springer.com/book/10.1007/978-3-642-37453-1 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7819 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Pacific-Asia Conference on Knowledge Discovery and Data Mining 2013
Abbreviated title	PAKDD 2013
Country/Territory	Australia
City	Gold Coast
Period	14/04/13 → 17/04/13
Internet address	https://link.springer.com/book/10.1007/978-3-642-37453-1 (Proceedings)

Keywords

Medical data
Sparse representation
Subspace clustering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-642-37456-2_11

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Saha, B., Pham, D. S., Phung, D., & Venkatesh, S. (2013). Clustering patient medical records via sparse subspace representation. In Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings (PART 2 ed., pp. 123-134). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7819 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-37456-2_11

Saha, Budhaditya ; Pham, Duc Son ; Phung, Dinh et al. / Clustering patient medical records via sparse subspace representation. Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings. PART 2. ed. 2013. pp. 123-134 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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Saha, B, Pham, DS, Phung, D & Venkatesh, S 2013, Clustering patient medical records via sparse subspace representation. in Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7819 LNAI, pp. 123-134, Pacific-Asia Conference on Knowledge Discovery and Data Mining 2013, Gold Coast, Queensland, Australia, 14/04/13. https://doi.org/10.1007/978-3-642-37456-2_11

Clustering patient medical records via sparse subspace representation. / Saha, Budhaditya; Pham, Duc Son; Phung, Dinh et al.
Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings. PART 2. ed. 2013. p. 123-134 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7819 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AB - 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 ℓ1 -regularized optimization algorithms. Using a cohort of both diabetes and stroke data we show that we outperform existing benchmark clustering techniques in the literature.

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Saha B, Pham DS, Phung D, Venkatesh S. Clustering patient medical records via sparse subspace representation. In Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings. PART 2 ed. 2013. p. 123-134. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-37456-2_11

Clustering patient medical records via sparse subspace representation

Abstract

Publication series

Conference

Keywords

UN SDGs

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