Descripción del proyecto
Predecir la depresión para facilitar su prevención
La prevención es el método más eficaz para abordar la depresión, que afecta a cerca de cuarenta millones de personas en la región europea. La depresión limita la capacidad de una persona para hacer frente a la vida diaria y rendir plenamente en el trabajo o la escuela, y constituye la causa principal de muerte por suicidio. Los estudios revelan que solo el 50 % de las personas deprimidas mejoran con el tratamiento inicial. En este contexto, en el proyecto WARN-D, financiado por el Consejo Europeo de Investigación, se desarrollará un sistema de alerta rápida personalizado para ayudar a identificar a las personas en riesgo y a las que deben recibir asistencia. El equipo del proyecto llevará a cabo un seguimiento a dos mil personas en su vida diaria durante más de dos años. Se emplearán múltiples herramientas a fin de desarrollar el sistema dirigido a predecir la depresión antes de que aparezca.
Objetivo
Depression is common, debilitating, and often chronic. It severely compromises the capacity for independent living, and is the strongest predictor of suicide. Young people are disproportionately affected, and many will spend over 20% of their lives in a state of depression. Further, only 50% of patients improve under initial treatment. Experts agree that prevention is the most effective way to change depression’s global disease burden. The biggest barrier to successful personalized prevention is to identify those at risk for depression in the near future. My proposal aims to solve the challenge who should receive prevention, and when, by developing the personalized early warning system WARN-D. To implement personalized detection, I will follow 2,000 individuals over 2 years, and integrate emerging theoretical, measurement, and modelling approaches from different scientific fields so far unconnected. Regarding theory, I conceptualize depression as a complex dynamical system in which causal relations and vicious cycles between problems can move the system from a healthy to a clinical state, consistent with the Network Approach to Psychopathology that I co-developed. Regarding measurement, I will follow participants in their daily lives, and collect temporal dynamics of bio-psycho-social variables like mood, anxiety, stress, impairment, sleep, and activity via smart-phone based ecological momentary assessment (EMA) and smart-watch based digital phenotype data. I will use dynamical network models to study the relations among problems, and use parameters of these models, combined with baseline, EMA, and digital phenotype data, to construct the prediction model WARN-D via state-of-the-art machine learning models. The interdisciplinary project combines numerous modern tools to develop a tailored personalized early warning system that forecasts depression reliably before it occurs, promising to radically transform the science of depression detection.
Ámbito científico
- natural sciencesmathematicsapplied mathematicsdynamical systems
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsmobile phones
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programa(s)
Régimen de financiación
ERC-STG - Starting GrantInstitución de acogida
2311 EZ Leiden
Países Bajos