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

    Access Status
    Fulltext not available
    Authors
    Nguyen, T.
    Tran, The Truyen
    Phung, D.
    Venkatesh, S.
    Date
    2013
    Type
    Conference Paper
    
    Metadata
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    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.
    Source Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    DOI
    10.1007/978-3-642-37453-1_11
    ISBN
    9783642374524
    School
    Multi-Sensor Proc & Content Analysis Institute
    URI
    http://hdl.handle.net/20.500.11937/12693
    Collection
    • Curtin Research Publications
    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.

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