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.
Professor
Supervisor of Doctorate Candidates
Supervisor of Master's Candidates
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
Open time:..
The Last Update Time:..