Descripción del proyecto
¿Puede el análisis del habla ayudar a prevenir la psicosis?
Los trastornos psicóticos suelen tener una evolución creciente y decreciente, y las personas vulnerables corren el riesgo de recaer. A menudo, las recaídas en los trastornos psicóticos pueden prevenirse cuando se recibe una advertencia a tiempo, de modo se que puedan tomar las medidas oportunas. En el proyecto TRUSTING, financiado con fondos europeos, se trabaja con la hipótesis de que el habla espontánea alberga la clave para predecir si la psicosis es inminente. El enfoque utilizado por el equipo del proyecto consistirá en pedir a las personas con riesgo de recaída que graben breves muestras de habla a intervalos regulares. Estas muestras se analizarán con inteligencia artificial a fin de reconocer los cambios que indican síntomas psicóticos sutiles. El objetivo del proyecto es desarrollar una aplicación fácil de usar y fiable, que se pueda utilizar desde casa, para enviar un mensaje cuando se detecten desviaciones del habla que predigan una recaída.
Objetivo
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.
Ámbito científico
Palabras clave
Programa(s)
Convocatoria de propuestas
HORIZON-HLTH-2022-STAYHLTH-01-two-stage
Consulte otros proyectos de esta convocatoriaRégimen de financiación
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinador
9713 GZ Groningen
Países Bajos