Clustering Patients with Tensor Decomposition
Machine Learning
2017-08-31 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.1708.08994,
title = {Clustering Patients with Tensor Decomposition},
author = {Matteo Ruffini and Ricard Gavaldà and Esther Limón},
journal= {arXiv preprint arXiv:1708.08994},
year = {2017}
}
Comments
Presented at 2017 Machine Learning for Healthcare Conference (MLHC 2017). Boston, MA