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TRUSTING

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A TRUSTworthy speech-based AI monitorING system for the prediction of relapse in...

A TRUSTworthy speech-based AI monitorING system for the prediction of relapse in individuals with schizophrenia Schizophrenia affects a staggering 21 million people worldwide, with 80% of these citizens suffering from a relapsing disease, putting their health and safety at enormous risk. Timely detection of these psychotic relapses would re... ver más

31/12/2028

UNIVERSITAIR MEDIS...

6M€

Presupuesto del proyecto: 6M€

Líder del proyecto

ACADEMISCH ZIEKENHUIS GRONINGEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-07-01

HORIZON-HLTH-2022-STAYHLTH-01-05-two-stage: Prevention of obesity throughout th... Expected Outcome:This topic aims at supporting activities that are enabling or contributing to one o...

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

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UNIVERSITAIR MEDISCH CENTRUM...

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Duración del proyecto: 65 meses Fecha Inicio: 2023-07-01
Fecha Fin: 2028-12-31

Líder del proyecto

ACADEMISCH ZIEKENHUIS GRONINGEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 6M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Schizophrenia affects a staggering 21 million people worldwide, with 80% of these citizens suffering from a relapsing disease, putting their health and safety at enormous risk. Timely detection of these psychotic relapses would require very frequent contact with clinicians, which is neither desirable nor feasible. An accurate online relapse predictor could alert clinicians of subtle deterioration, which enables timely intervention and allow safe discontinuation of long-term medication, which so many affected citizens desire. Our Consortium demonstrated that subtle alterations in speech carry a predictive signal for psychosis onset. This project will develop an AI monitoring system that leverages spoken language processing (SLP) and natural language processing (NLP) of speech recorded at home to calculate the relapse risk. The monitoring tool we develop will be validated retrospectively in a longitudinal cohort, cross-sectionally, across six languages, after which it will be tested prospectively in a multicenter randomized trial, with the end goal of improving functional and clinical outcomes of those affected by schizophrenia. Developing such a system for exceptionally vulnerable people requires ‘buy-in’ from clinicians and mental health care service users, namely trust. A lack of trust is the biggest obstacle to the real-world implementation of a speech-based monitoring system. TRUSTING will develop a framework that systematically ensures addressing all the criteria for trustworthy AI put forward by the EU. This will ensure an empirically based and validated tool that can reliably detect pending relapse. As the core philosophy of trustworthiness is part of every aspect of the project, it will be a system more likely to be welcomed and embraced by service users and their carers. TRUSTING generates the scientific and social foundation for disruptive technology to deliver the unmet promise of an equitable and just form of healthcare for people at risk of relapse.

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