Clustering Patients with Tensor Decomposition

Matteo Ruffini, Ricard Gavalda, Esther Limon
Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:126-146, 2017.

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 this Paper

BibTeX
@InProceedings{pmlr-v68-ruffini17a, title = {Clustering Patients with Tensor Decomposition}, author = {Ruffini, Matteo and Gavalda, Ricard and Limon, Esther}, booktitle = {Proceedings of the 2nd Machine Learning for Healthcare Conference}, pages = {126--146}, year = {2017}, editor = {Doshi-Velez, Finale and Fackler, Jim and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {68}, series = {Proceedings of Machine Learning Research}, month = {18--19 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v68/ruffini17a/ruffini17a.pdf}, url = {https://proceedings.mlr.press/v68/ruffini17a.html}, 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.} }
Endnote
%0 Conference Paper %T Clustering Patients with Tensor Decomposition %A Matteo Ruffini %A Ricard Gavalda %A Esther Limon %B Proceedings of the 2nd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2017 %E Finale Doshi-Velez %E Jim Fackler %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v68-ruffini17a %I PMLR %P 126--146 %U https://proceedings.mlr.press/v68/ruffini17a.html %V 68 %X 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.
APA
Ruffini, M., Gavalda, R. & Limon, E.. (2017). Clustering Patients with Tensor Decomposition. Proceedings of the 2nd Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 68:126-146 Available from https://proceedings.mlr.press/v68/ruffini17a.html.

Related Material