Detecting dementia from written and spoken language
Vaden Masrani · cIRcle (University of British Columbia) · 2018
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.