Computer Science graduate student Katie Fraser (PhD) and Assistant Professor and Scientist with the Toronto Rehabilitation Institute (UHN) Dr. Frank Rudzicz (PhD 2011) are part of a study that will result in easier, more cost-effective and accurate assessment in dementia screening.
Fraser, Rudzicz and colleagues have discovered how to diagnose Alzheimer's disease with more than 82 per cent accuracy by evaluating the interplay between four linguistic factors, using hundreds of individual variables; and developing automated technology to detect these impairments.
Based on the analysis, it was determined that four collective dimensions of speech are indicative of dementia: semantic impairment, such as using overly simple words; acoustic impairment, such as speaking more slowly; syntactic impairment, such as using less complex grammar; and information impairment, such as not clearly identifying the main aspects of a picture.
"Previous to our study, language factors were connected to Alzheimer's disease, but often only related to delayed memory or a person's ability to follow instructions," says Dr. Rudzicz, who is also a Network Investigator with the AGE-WELL Network of Centres of Excellence. "This study characterizes the diversity of language impairments experienced by people with Alzheimer's disease, and our automated detection algorithm takes this into account."
Rudzicz is a member of the Computational Linguistics group, an area of focus within Artificial Intelligence and his work is part of an emerging area in Health & Assistive Technology.
The research is published in the December issue of the Journal of Alzheimer's Disease.
This study was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Alzheimer's Association, and the Alzheimer's Society.
– Read the full UNH Press Release
– Follow Dr. Rudzicz's lab on Twitter