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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 VenkateshEfficient 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 IPagination
123 - 135Publisher
SpringerLocation
Gold Coast, QueenslandPlace of publication
Berlin, GermanyPublisher DOI
Start date
2013-04-14End date
2013-04-17ISBN-13
9783642374531ISBN-10
3642374530Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, SpringerExtent
49Editor/Contributor(s)
J Pei, V Tseng, L Cao, G Xu, H MotodaTitle of proceedings
PAKDD 2013 : Advances in knowledge discovery and data mining : 17th Pacific-Asia Conference, Gold Coast, Australia, April 14-17, 2013 : proceedingsUsage metrics
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