Clustering Patient Medical Records via Sparse Subspace Representation
MetadataShow full item record
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.
Showing items related by title, author, creator and subject.
Gupta, Sunil Kumar (2011)The growing number of information sources has given rise to joint analysis. While the research community has mainly focused on analyzing data from a single source, there has been relatively few attempts on jointly analyzing ...
Pham, DucSon; Arandjelovic, O.; Venkatesh, S. (2016)We propose an effective subspace selection scheme as a post-processing step to improve results obtained by sparse subspace clustering (SSC). Our method starts by the computation of stable subspaces using a novel random ...
Li, Q.; Liu, Wan-Quan; Li, Ling (2018)© 2018 Elsevier Ltd Subspace clustering refers to the problem of finding low-dimensional subspaces (clusters) for high-dimensional data. Current state-of-the-art subspace clustering methods are usually based on spectral ...