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Latent Patient Profile Modelling and Applications with Mixed-Variate Restricted Boltzmann Machine

机译：混合变量受限玻尔兹曼机的潜在患者档案建模和应用

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摘要

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.
机译：慢性病的有效管理在现代医疗保健中至关重要。我们考虑糖尿病，而我们目前的目标是研究机器学习如何为临床效率提供信息。面临的挑战是如何汇总高度异质的资源，包括人口统计学，诊断，病理和治疗方法，并提取相似的人群，以便设计护理计划。为此，我们扩展了我们的最新模型，即混合变量受限Boltzmann机（MV.RBM），因为该模型无缝集成了随时间推移汇总的每个患者的多种数据类型，并输出称为“潜在特征”的同类表示形式，用于患者聚类，可视化，疾病相关分析和预测。我们证明了该方法在这些任务上的表现优于所有基线-能够确定同一组患者的主要特征，并且可以为诊断代码预测取得良好的结果。

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  • 会议地点 Gold Coast(AU)
  • 作者

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

  • 作者单位

    Center for Pattern Recognition and Data Analytics School of Information Technology, Deakin University, Geelong, Australia;

    Center for Pattern Recognition and Data Analytics School of Information Technology, Deakin University, Geelong, Australia;

    Center for Pattern Recognition and Data Analytics School of Information Technology, Deakin University, Geelong, Australia;

    Center for Pattern Recognition and Data Analytics School of Information Technology, Deakin University, Geelong, Australia;

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