Imagine a GPS for heart health that doesn't just tell you where you are, but predicts exactly where your health is heading. Instead of a one-size-fits-all treatment, this system uses a massive library of patient data to find the right medicine for your specific body type. It's like moving from a generic clothing store to a custom tailor for heart care.
Heart failure has a 20-50% five-year mortality rate because current care is too generic. There is a critical need for tools that can predict which specific patients will respond to which treatments to reduce healthcare costs and mortality.
AI-driven prediction models for disease risk and treatment response, and a blockchain-supported federated data infrastructure for secure patient data analysis.
If you are a drug development firm dealing with high clinical trial failure rates — this project developed prediction models for treatment response based on biomarker profiles. This allows you to identify the specific patient subgroups most likely to benefit from a therapy, reducing trial risk.
If you are a medical software provider dealing with generic risk scores that lack precision — this project developed AI-based individual disease prediction models. You can integrate these tools to offer clinicians a way to divide patients into clinically meaningful subgroups for better care.
If you are a hospital network dealing with the high costs of heart failure readmissions — this project developed cost-effective and scalable patient-oriented care pathways. This helps you move from reactive treatment to early, individualized prevention.
Based on available project data, specific pricing or cost structures for the resulting tools are not provided.
Yes, the project is designed for scale, utilizing a federated database covering over 1,000,000 patients across 14 countries.
Based on available project data, specific IP and licensing terms are not detailed, though the project involves 22 industry partners.
The project uses a blockchain-supported federated database and a data clean room to ensure secure, anonymous access to patient-level data.
The project runs from October 1, 2023, to March 31, 2028, with current progress moving from foundational work to integrated implementation.
The consortium is heavily industry-weighted with 22 industrial partners (51% of the total), including 7 SMEs. This high ratio of private sector involvement, combined with 11 universities and 10 research institutes across 14 countries, suggests a strong focus on commercial viability and practical application rather than purely academic research.
Contact Universiteit Maastricht regarding the iCARE4CVD consortium
Contact SciTransfer to identify licensing opportunities for the AI prediction models.
