Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine
dc.contributor.author | Nguyen, T. | |
dc.contributor.author | Tran, The Truyen | |
dc.contributor.author | Phung, D. | |
dc.contributor.author | Venkatesh, S. | |
dc.date.accessioned | 2017-01-30T11:32:15Z | |
dc.date.available | 2017-01-30T11:32:15Z | |
dc.date.created | 2015-10-29T04:09:59Z | |
dc.date.issued | 2013 | |
dc.identifier.citation | Nguyen, T. and Tran, T.T. and Phung, D. and Venkatesh, S. 2013. Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine, pp. 123-135. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/12693 | |
dc.identifier.doi | 10.1007/978-3-642-37453-1_11 | |
dc.description.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. © Springer-Verlag 2013. | |
dc.title | Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine | |
dc.type | Conference Paper | |
dcterms.source.volume | 7818 LNAI | |
dcterms.source.number | PART 1 | |
dcterms.source.startPage | 123 | |
dcterms.source.endPage | 135 | |
dcterms.source.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dcterms.source.series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dcterms.source.isbn | 9783642374524 | |
curtin.department | Multi-Sensor Proc & Content Analysis Institute | |
curtin.accessStatus | Fulltext not available |
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