Project description
AI-based continuous stratification for stroke patients
Stroke patient stratification currently relies on advanced machine learning algorithms trained on vast amounts of data. However, these models can only consider some of the diverse and varied data generated about a patient, and stratification is only performed intermittently. The EU-funded STRATIF-AI project uses continuous stratification with the new STRATIF-AI platform. Patient data is stored in their own personal data vault, continuously updated in its digital twin system. This hybrid architecture combines mechanistic models with machine learning and bioinformatics to simulate patient-specific change responses and observe changes on various levels, ranging from seconds to years. The project uses advanced technology to connect apps and track a patient's stroke journey, from prevention to rehabilitation.
Objective
State-of-the-art stratification today is based on machine-learning (ML) algorithms, trained on large cohort data. This has two main limitations: a) such ML-models cannot use all the variety of different data that is generated about a patient, b) stratification is thus only done intermittently, implying out-dated and sub-optimal care decisions. To remedy this, we herein present a new concept and technology - continuous stratification, using our new STRATIF-AI platform. In continuous stratification, all data generated about a patient is cumulatively stored in a Personal Data Vault, controlled by the patient. These personal data continuously updates our world-unique digital twins. The unique potential with our twins comes from the hybrid architecture, combining mechanistic, multi-scale, and multi-organ models with ML and bioinformatics. This allows us to simulate patient-specific responses to changes in diet, exercise, and certain medications, and see changes on both an intracellular, organ, and whole-body level, ranging from seconds to years. We also combine semantic harmonization with federated learning to securely re-train the various sub-models, when new data become available in one of the cohort databases. In this project, we will for the first time use this cutting-edge technology to connect a series of apps that together covers an entire patient journey. Using 6 new clinical studies, involving 8 new partner hospitals, we will both refine and validate the models, and demonstrate how the same digital twin can follow a patient across different apps, covering all phases of stroke: from prevention, to acute treatment, and rehabilitation. Our scalable platform for continuous stratification forms the foundation for a new interconnected and patient-centric healthcare system.
Fields of science
- natural sciencescomputer and information sciencesdatabases
- medical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugs
- medical and health sciencesclinical medicinephysiotherapy
- medical and health scienceshealth sciencesnutrition
- medical and health sciencesbasic medicineneurologystroke
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
581 83 Linkoping
Sweden