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STRATIF-AI

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Continuous stratification for improved prevention treatment and rehabilitation...

Continuous stratification for improved prevention treatment and rehabilitation of stroke patients using digital twins and AI 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 abou... ver más

30/04/2027

Líder desconocido

6M€

Presupuesto del proyecto: 6M€

Líder del proyecto Líder desconocido

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-04-21

HORIZON-HLTH-2022-TOOL-12-01-two-stage: Computational models for new patien... Expected Outcome:This topic aims at supporting activities that are enabling or contributing to one o...

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Duración del proyecto: 48 meses Fecha Inicio: 2023-04-21
Fecha Fin: 2027-04-30

Líder del proyecto Líder desconocido

Presupuesto del proyecto 6M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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