Opis projektu
Lepsza profilaktyka depresji oparta na prognozowaniu
Profilaktyka to najlepsza metoda walki z depresją, która dotyka około 40 milionów osób w Europie. Depresja, główna przyczyna śmierci samobójczych, utrudnia codzienne funkcjonowanie oraz ogranicza zdolność do nauki lub wykonywania pracy. Badania wskazują, że wstępne leczenie przynosi poprawę jedynie u 50 % pacjentów. W związku z tym zespół finansowanego ze środków ERBN projektu WARN-D opracuje spersonalizowany system wczesnego ostrzegania, który ma ułatwić identyfikację osób znajdujących się w grupie ryzyka i potrzebujących pomocy. W ramach projektu przez 2 lata prowadzona będzie codzienna obserwacja 2 000 osób. System umożliwiający przewidywanie depresji, zanim ona wystąpi, powstanie w oparciu o wiele narzędzi.
Cel
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.
Dziedzina nauki
- natural sciencesmathematicsapplied mathematicsdynamical systems
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsmobile phones
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Program(-y)
Temat(-y)
System finansowania
ERC-STG - Starting GrantInstytucja przyjmująca
2311 EZ Leiden
Niderlandy