In the next five years, the senior population in Canada is projected to exceed 9.5 million individuals, comprising approximately 23 per cent of the total population.
The growing number of older adults will result in increased complex age-related conditions (CACs), including injuries from falls and symptoms of Parkinson's disease and dementia, putting significant pressure on the Canadian health-care system.
To help address these challenges, Dr. Mina Nouredanesh, assistant professor of community health sciences at the Max Rady College of Medicine in the Rady Faculty of Health Sciences, has been appointed a Canada Research Chair (Tier 2) in artificial intelligence (AI) for complex health data.
This prestigious appointment recognizes Nouredanesh's pioneering research to develop innovative solutions for age-related conditions and alleviate stress on populations, caregivers and the health-care system. She brings 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 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 of 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 such as blood tests and medical imaging, and free-living data collected by wearable sensors such as smart watches that older adults can wear in their everyday environments.
Nouredanesh will use AI to expand personalized medicine and improve diagnostic, prognostic and treatment methods. While AI has shown promise in addressing health problems, she says, it is in the early stages of development when it comes to predicting and managing CACs, such as falling.
The scientist hopes that this work will assist in the diagnosis and management of age-related conditions and will help to improve functioning in older adults, enhancing their independence. Ultimately, she says, personalized assistive technologies could reduce health-system burdens and contribute significantly to older adults' quality of life.
Research at the University of Manitoba is partially supported by funding from the Government of Canada Research Support Fund.
