The Five Phases of My Research:

1. Identifying At-Risk Populations with AI
   Using machine learning and large-scale Danish health registers, we analyze data to pinpoint geographic areas and individuals at the highest risk of stroke. This AI-driven approach allows for early intervention and targeted prevention efforts.
   
   
2. Learning from International Best Practices
   Stroke prevention is a global challenge. We investigate successful international case studies and emerging health technologies to understand how other countries effectively engage citizens and integrate innovative healthcare solutions.
   
   
3. Testing Cuffless Blood Pressure Monitoring Technology
   Traditional blood pressure cuffs are often inconvenient and underutilized. In partnership with Netcompany and international experts, we identify and test new, non-invasive blood pressure monitoring devices that can integrate seamlessly with smartphones, watches, and wearables.
   
   
4. Real-World Testing in Danish Retail Stores
   In collaboration with Hjernesagen, Salling Fondene, and Salling Group, we introduce new blood pressure monitoring technology in Bilka, føtex, and Netto stores. By comparing traditional cuff-based measurements with innovative, mobile health solutions, we evaluate their effectiveness and ease of use.
   
   
5. Creating a Scalable Model for Stroke Prevention
   The insights gained throughout the research will be used to develop a national roll-out plan, ensuring that AI-driven stroke prevention becomes an integral part of Denmark’s healthcare system—with the potential to expand globally.
   
   The PhD research project is sponsored by TrygFonden, Salling Fondene and Netcompany.