Applying machine learning to detect Alzheimer's Disease symptoms

November 29, 2018

Drs. Cristina Conati and Thalia Field were awarded funds for their project "Leveraging eye-tracking data to improve reliable detection of Alzheimer’s Disease and related patient’s states." These postdoctoral funds will allow the team to recruit an experienced and talented postdoc to develop and apply new machine learning algorithms to detect Alzheimer's Disease symptoms as a patient goes about their daily activities. This project is possible with support from the Vancouver Coastal Health Research Institute and Pacific Health Innovation Exchange. A summary of the project follows below:

Reliable detection of disease in the early stages of Alzheimer’s Disease (AD) continues to be a challenge. This project led by Drs. Conati and Field aims to investigate the value of eye-tracking data as one of the sources of information to build machine learning detectors of AD. In addition, the team will investigate eye-tracking based detectors of AD-related states such confusion and distress during naturally occurring tasks. This project aims to 1) validate the concept of using spontaneous speech and eye tracking data as clinical markers for early detection of AD through the use of machine-learning algorithms; and 2) investigate ways to increase detection accuracy by exploring both alternative machine learning settings, as well as alternative diagnostic tasks used for data collection. Eventually, the goal is to develop software that can detect states of confusion or distress in AD patients during day-to-day activities (e.g., reading an article) and automatically trigger interventions aimed at reducing the levels of discomfort and stress.