Clustering Patients with Tensor Decomposition

In this paper we present a method for the unsupervised clustering of high-dimensional binary data, with a special focus on electronic healthcare records. We present a robust and efficient heuristic to face this problem using tensor decomposition. We present the reasons why this approach is preferabl...

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Bibliographic Details
Published in:arXiv.org 2017-08
Main Authors: Ruffini, Matteo, Gavaldà, Ricard, Limón, Esther
Format: Article
Language:English
Subjects:
Algorithms
Binary data
Clustering
Decomposition
Electronic health records
Health care
Mathematical analysis
Tensors
Online Access:Get full text
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Summary:In this paper we present a method for the unsupervised clustering of high-dimensional binary data, with a special focus on electronic healthcare records. We present a robust and efficient heuristic to face this problem using tensor decomposition. We present the reasons why this approach is preferable for tasks such as clustering patient records, to more commonly used distance-based methods. We run the algorithm on two datasets of healthcare records, obtaining clinically meaningful results.
ISSN:2331-8422