Descripción del proyecto
Estratificación continua basada en inteligencia artificial para pacientes afectados por un ictus
La estratificación de pacientes que han sufrido un ictus se basa en algoritmos avanzados de aprendizaje automático entrenados con grandes conjuntos de datos. Sin embargo, estos modelos solo consideran algunos de los múltiples y heterogéneos datos de un paciente, y la estratificación se realiza de forma intermitente. En el proyecto STRATIF-AI, financiado con fondos europeos, se empleará la estratificación continua con la nueva plataforma STRATIF-AI. Los datos de los pacientes se almacenan en su propia bóveda de datos personal, actualizada continuamente en su sistema gemelo digital. Esta arquitectura híbrida combina modelos mecanicistas con aprendizaje automático y bioinformática para simular respuestas de cambio específicas de cada paciente y observar los cambios en varios niveles, desde segundos hasta años. En el proyecto se utilizará tecnología avanzada para conectar aplicaciones y seguir el itinerario de un paciente que ha sufrido un ictus, desde la prevención hasta la rehabilitación.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
Convocatoria de propuestas
HORIZON-HLTH-2022-TOOL-12-two-stage
Consulte otros proyectos de esta convocatoriaRégimen de financiación
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinador
581 83 Linkoping
Suecia