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AI-PREVENT

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A nationwide artificial intelligence risk assessment for primary prevention of c...

A nationwide artificial intelligence risk assessment for primary prevention of cardiometabolic diseases Diabetes, stroke and coronary artery disease (cardiometabolic diseases) are the leading cause of death in Europe. Given that effective pharmacological and lifestyle interventions are available, it is important to identify high ris... ver más

31/12/2025

HELSINGIN YLIOPIST...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

HELSINGIN YLIOPISTO No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-12-03

ERC-2020-STG: ERC STARTING GRANTS Scope:Objectives

I+D

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

HELSINGIN YLIOPISTO

1.55M€ | Lider

KAMAX

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Duración del proyecto: 60 meses Fecha Inicio: 2020-12-03
Fecha Fin: 2025-12-31

Líder del proyecto

HELSINGIN YLIOPISTO No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Diabetes, stroke and coronary artery disease (cardiometabolic diseases) are the leading cause of death in Europe. Given that effective pharmacological and lifestyle interventions are available, it is important to identify high risk individuals at an early stage. Traditionally, this is done using clinical prediction models. However, the established models have substantial limitations: they are often used by doctors only when an underlying disease is already suspected, they are not developed on updated nationally-representative data and they require time-consuming clinical measurements. Thus, a substantial part of the population is not provided with risk assessment. I propose to revolutionize the existing approaches to primary prevention by providing risk assessment of cardiometabolic diseases before an individual even steps into the doctor’s office for a visit. To this end my project has three main objectives: 1) Development of artificial intelligence (AI) approaches to model health trajectories based on nationwide registry data on medications, diagnoses, familial risk and socio-demographic information to obtain accurate risk estimates for cardiometabolic disease. I will integrate high quality data from selected countries that have long traditions of registry data (Finland and Sweden, over 7.5 million individuals). 2) To identify health trajectories that maximize the clinical utility of genetic scores by integrating genetic and registry-based data on > 1 million people to identify subgroups of individuals for whom genetic information might improve risk prediction. 3) Validation of AI and genetic-based risk assessment as first-stage screening via a clinical study in 2800 individuals. My project leverages the latest developments in AI and high-quality data of unprecedented scale to deliver a paradigm shift with important public health consequences by potentially changing the way cardiometabolic disease risk is assessed.

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