New Canada Research Chair using AI to improve quality of life in older populations
In the next five years, the senior population in Canada is projected to exceed 9.5 million individuals, comprising approximately 23 percent of the total population.
The growing number of older adults will result in increased complex age-related conditions (CACs), including injury from falls, symptoms from Parkinson’s disease and dementia, amongst others, putting significant pressure on the Canadian healthcare system.
To help address these challenges, Dr. Mina Nouredanesh, assistant professor in the Department of Community Health Sciences in the Max Rady College of Medicine at the Rady Faculty of Health Sciences has been appointed a Canada Research Chair (Tier 2) in AI for Complex Health Data. This prestigious appointment recognizes Dr. Nouredanesh’s pioneering research to develop innovative solutions for age-related conditions and alleviate stress on populations, caregivers and the healthcare system. She offers a multidisciplinary lens to this research, owing to her extensive experience in engineering, machine learning and health data analysis.
“My goal is to design innovative AI-powered personalized tools to help understand and treat the many factors that contribute to CACs and improve the lives of older adults and their caregivers,” said Nouredanesh.
Despite many technological advancements in recent years, knowledge gaps persist, including a lack of precise tools to proactively assess individual-level risks associated with CACs. Every case is unique due to the complexity of symptoms or injury experienced by older adults.
“There are no effective cures to many CACs, so identifying early signs, well in advance of their onset, or detecting factors that trigger them in those already affected, is crucial for developing targeted interventions to delay their progression and mitigate impact,” says Dr. Nouredanesh. “One-size-fits-all prevention and rehabilitation strategies often fall short because each individual may experience a specific interplay between various risk factors that contribute to the development of these adverse conditions,” she adds.
Nouredanesh will address the complex nature of CACs by looking at multiple types information, bringing together physical, genetic, psychological, socioeconomic, behavioural and environmental data from a variety of sources. Her work will address critical questions, such as:
- What factors are sensitive to early signs of a CAC in an individual?
- What contexts in everyday scenarios trigger a CAC in a symptomatic individual?
- How to intervene?”
To answer these questions, Nouredanesh will use questionnaires, in-lab data (e.g., blood test results and medical imaging), and free-living data collected by wearable sensors (e.g., a smart watch) in their everyday environments (i.e., at home). Nouredanesh will use AI to expand personalized medicine and improve diagnostic, prognostic and treatment methods. While AI has shown promise in addressing health problems, it is in the early stages of development when it comes to predicting and managing CACs, such as falling.
This impactful work will lead to novel interventions for the diagnosis and management of age-related conditions and will help improve functioning in older adults to provide independence that is so often lost in aging. Ultimately, these UM-designed personalized assistive technologies will help older adults and their care-givers manage complex age-related conditions and could reduce associated economic burdens by translating the research findings into health policy to create wide-spread benefit to society and quality of life.
Research at the University of Manitoba is partially supported by funding from the Government of Canada Research Support Fund.