Cited By
View all- Lu MZhang YZhang SShi HHuang Z(2023)Knowledge-aware patient representation learning for multiple disease subtypesJournal of Biomedical Informatics10.1016/j.jbi.2023.104292138:COnline publication date: 1-Feb-2023
Multi-view representation learning via gcca for multimodal analysis of Parkinson's disease
Authors: 
J. C. Vásquez-Correa, J. R. Orozco-Arroyave, R. Arora, E. Nöth, N. Dehak, H. Christensen, F. Rudzicz, T. Bocklet, M. Cernak, H. Chinaei, J. Hannink, Phani Sankar Nidadavolu, M. Yancheva, A. Vann, N. VoglerAuthors Info & Claims
J. C. Vásquez-Correa, J. R. Orozco-Arroyave, R. Arora, E. Nöth, N. Dehak, H. Christensen, F. Rudzicz, T. Bocklet, M. Cernak, H. Chinaei, J. Hannink, Phani Sankar Nidadavolu, M. Yancheva, A. Vann, N. VoglerAuthors Info & ClaimsPages 2966 - 2970
Abstract
Information from different bio-signals such as speech, handwriting, and gait have been used to monitor the state of Parkinson's disease (PD) patients, however, all the multimodal bio-signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for learning a representation of features extracted from handwriting and gait that can be used as a complement to speech-based features. Three different problems are addressed: classification of PD patients vs. healthy controls, prediction of the neurological state of PD patients according to the UPDRS score, and the prediction of a modified version of the Frenchay dysarthria assessment (m-FDA). According to the results, the proposed approach is suitable to improve the results in the addressed problems, specially in the prediction of the UPDRS, and m-FDA scores.
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View all- Lu MZhang YZhang SShi HHuang Z(2023)Knowledge-aware patient representation learning for multiple disease subtypesJournal of Biomedical Informatics10.1016/j.jbi.2023.104292138:COnline publication date: 1-Feb-2023