Résumé des résultats (Abstract)
(Anglais)
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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 worldunique
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.
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