Bo Jin
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Paper Publications
An RNN architecture with dynamic temporal matching for personalized predictions of Parkinson's disease
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Indexed by:会议论文

Date of Publication:2017-04-27

Included Journals:EI

Page Number:198-206

Abstract:Parkinson's disease (PD) is a chronic disease that develops over years and varies dramatically in its clinical manifestations. A preferred strategy to resolve this heterogeneity and thus enable better prognosis and targeted therapies is to segment out more homogeneous patient sub-populations. However, it is challenging to evaluate the clinical similarities among patients because of the longitudinality and temporality of their records. To address this issue, we propose a deep model that directly learns patient similarity from longitudinal and multi-modal patient records with an Recurrent Neural Network (RNN) architecture, which learns the similarity between two longitudinal patient record sequences through dynamically matching temporal patterns in patient sequences. Evaluations on real world patient records demonstrate the promising utility and efficacy of the proposed architecture in personalized predictions. Copyright © by SIAM.

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Gender:Male

Alma Mater:Dalian University of Technology

Degree:Doctoral Degree

School/Department:Dalian University of Technology

Discipline:Computer Applied Technology

Business Address:816 Yanjiao Building, Dalian University of Technology

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