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Detecting dementia from written and spoken language

University of British Columbia

This thesis makes three main contributions to existing work on the automatic detection of dementia from language. First we introduce a new set of biologically motivated spatial neglect features, and show their inclusion achieves a new state of the art in classifying Alzheimer's disease (AD) from recordings of patients undergoing the Boston Diagnostic Aphasia Examination. Second we demonstrate how a simple domain adaptation algorithm can be used to leveraging AD data to improve classification of mild cognitive impairment (MCI), a condition characterized by a slight-but-noticeable decline in cognition that does not meet the criteria for dementia, and a condition for which reliable data is scarce. Third, we investigate whether dementia can be detected from written rather than spoken language, and show a range of classifiers achieve a performance far above baseline. Additionally, we create a new corpus of blog posts written by authors with and without dementia and make it publicly available for future researchers.

Text

2018-01-08

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/64313

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/