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UBC Theses and Dissertations
Exploring neural models for predicting dementia from language Kong, Weirui
Abstract
In this thesis we explore the effectiveness of neural models that require no task-specific feature for automatic dementia prediction. The problem is about classifying Alzheimer's disease (AD) from recordings of patients undergoing the Boston Diagnostic Aphasia Examination (BDAE). First we use a multimodal neural model to fuse linguistic features and acoustic features, and investigate the performance change compared to simply concatenating these features. Then we propose a novel coherence feature generated by a neural coherence model, and evaluate the predictiveness of this new feature for dementia prediction. Finally we apply an end-to-end neural method which is free from feature engineering and achieves state-of-the-art classification result on a widely used dementia dataset. We further interpret the predictions made by this neural model from different angles, including model visualization and statistical tests.
Item Metadata
Title |
Exploring neural models for predicting dementia from language
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2019
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Description |
In this thesis we explore the effectiveness of neural models that require no task-specific feature for automatic dementia prediction. The problem is about classifying Alzheimer's disease (AD) from recordings of patients undergoing the Boston Diagnostic Aphasia Examination (BDAE). First we use a multimodal neural model to fuse linguistic features and acoustic features, and investigate the performance change compared to simply concatenating these features. Then we propose a novel coherence feature generated by a neural coherence model, and evaluate the predictiveness of this new feature for dementia prediction. Finally we apply an end-to-end neural method which is free from feature engineering and achieves state-of-the-art classification result on a widely used dementia dataset. We further interpret the predictions made by this neural model from different angles, including model visualization and statistical tests.
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Genre | |
Type | |
Language |
eng
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Date Available |
2019-08-09
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0380363
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2019-09
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International