Imagine if your physical therapist had a crystal ball that could predict exactly which exercises would work best for your specific body and lifestyle. Instead of trial and error, this system uses data from thousands of other patients to group people with similar needs. It helps doctors pick the right treatment plan the first time, making recovery faster and more reliable.
Healthcare providers lack reliable tools to predict which rehabilitation therapy will work for a specific patient. This leads to inefficient treatment selection, wasted resources, and poor patient outcomes.
AI prediction and stratification tools for medical data and a liaison platform for managing and sharing model results.
If you are a software developer dealing with generic rehab apps that don't show results — this project developed AI prediction and stratification tools that allow for personalized therapy strategies. This ensures patients get the right care based on their specific pathology, such as Parkinson's or stroke.
If you are a clinic manager dealing with high patient dropout rates due to ineffective treatments — this project developed a platform for managing model results that predicts outcomes. This helps in selecting the optimal therapy strategy to improve patient quality of life.
If you are a manufacturer dealing with inconsistent post-surgery recovery outcomes for hip and knee prosthesis — this project developed data-driven computational tools to stratify patients. This allows for better post-operative care plans tailored to the patient's living conditions.
Based on available project data, no specific pricing or cost structures are mentioned.
The project uses a federated way to combine real-world clinical datasets and targets 9 dominant causes for rehabilitation worldwide, suggesting a design intended for broad scale.
Based on available project data, the project exploits the open-science EHDEN platform, but specific licensing terms are not provided.
The project period runs from 2023-06-01 to 2027-05-31, indicating the tools are currently in development and validation.
It uses a common infrastructure for analyzing multiple observational data sources and includes tools to convert unstructured and structured health care data.
The consortium is well-balanced for commercialization, featuring a 35% industry ratio with 7 industrial partners, including 5 SMEs. With 20 partners across 9 countries, the project combines academic research from 7 universities with practical industrial application, increasing the likelihood of successful market entry.
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