ERC Starting Grant · 2020
Using Network Theory to Predict Depression Onset and Build a Personalized Early Warning System
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…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.