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Personalized Multi-Domain Digital Platform for Dementia Prevention
Seyed-Mohammad Fereshtehnejad1,2, Karim Keshavjee2, Abbas Zavar2
1Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Studies in Health Technology and Informatics
|February 19, 2025
Summary
This initiative uses a digital platform to create personalized dementia prevention plans. It analyzes health and lifestyle data to offer tailored, evidence-based recommendations for reducing dementia risk.
Area of Science:
- Neuroscience and Public Health
- Digital Health and AI in Medicine
Background:
- Dementia poses a significant global health burden.
- Current dementia prevention strategies often lack personalization.
- Integrating multi-domain risk factor data is crucial for effective prevention.
Purpose of the Study:
- To introduce the Personalized Multi-Domain Digital Platform for Dementia Prevention (PM(DP)*2).
- To outline an evidence-based approach for tailored dementia risk reduction.
- To leverage technology for dynamic and personalized prevention strategies.
Main Methods:
- Collection and analysis of multidimensional data on modifiable dementia risk factors (lifestyle, cardiovascular, environmental).
- Development of individualized dementia risk profiles.
- Utilization of machine learning and generative AI for personalized recommendations.
Main Results:
- Conceptualization of a platform generating individualized risk profiles.
- Design of a system for tailored, evidence-driven prevention strategies.
- Enhanced recommendation capabilities through AI and machine learning.
Conclusions:
- The PM(DP)*2 platform offers a novel, technology-driven approach to dementia prevention.
- Personalized interventions based on multi-domain data can effectively reduce dementia burden.
- AI and machine learning are key to delivering dynamic and evidence-based dementia prevention.
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