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Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine

conference contribution
posted on 2013-01-01, 00:00 authored by Tu Dinh Nguyen, Truyen TranTruyen Tran, Quoc-Dinh Phung, Svetha VenkateshSvetha Venkatesh
Efficient management of chronic diseases is critical in modern health care. We consider diabetes mellitus, and our ongoing goal is to examine how machine learning can deliver information for clinical efficiency. The challenge is to aggregate highly heterogeneous sources including demographics, diagnoses, pathologies and treatments, and extract similar groups so that care plans can be designed. To this end, we extend our recent model, the mixed-variate restricted Boltzmann machine (MV.RBM), as it seamlessly integrates multiple data types for each patient aggregated over time and outputs a homogeneous representation called "latent profile" that can be used for patient clustering, visualisation, disease correlation analysis and prediction. We demonstrate that the method outperforms all baselines on these tasks - the primary characteristics of patients in the same groups are able to be identified and the good result can be achieved for the diagnosis codes prediction.

History

Event

Advances in Knowledge Discovery and Data Mining. Pacific-Asia Conference (17th : 2013 : Gold Coast, Queensland)

Source

Advances in knowledge discovery and data mining : 17th Pacific-Asia Conference, PAKDD 2013 Gold Coast, Australia, April 14-17, 2013 Proceedings, Part I

Pagination

123 - 135

Publisher

Springer

Location

Gold Coast, Queensland

Place of publication

Berlin, Germany

Start date

2013-04-14

End date

2013-04-17

ISBN-13

9783642374531

ISBN-10

3642374530

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2013, Springer

Extent

49

Editor/Contributor(s)

J Pei, V Tseng, L Cao, G Xu, H Motoda

Title of proceedings

PAKDD 2013 : Advances in knowledge discovery and data mining : 17th Pacific-Asia Conference, Gold Coast, Australia, April 14-17, 2013 : proceedings

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