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.
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Saha, B., Pham, DS., Phung, D., Venkatesh, S. (2013). Clustering Patient Medical Records via Sparse Subspace Representation. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_11
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DOI: https://doi.org/10.1007/978-3-642-37456-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37455-5
Online ISBN: 978-3-642-37456-2
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