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

Tu Dinh Nguyen, Truyen Tran, Dinh Phung, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication17th Pacific-Asia Conference, PAKDD 2013 Gold Coast, Australia, April 14-17, 2013 Proceedings, Part I
EditorsJian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu
Place of PublicationBerlin Germany
PublisherSpringer
Pages123-135
Number of pages13
ISBN (Electronic)9783642374531
ISBN (Print)9783642374524
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2013 - Gold Coast, Australia
Duration: 14 Apr 201317 Apr 2013
Conference number: 17th
https://link.springer.com/book/10.1007/978-3-642-37453-1

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
NumberPART 1
Volume7818
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2013
Abbreviated titlePAKDD 2013
CountryAustralia
CityGold Coast
Period14/04/1317/04/13
Internet address

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