E. Fried: WARN-D: Building an early warning system for depression

Abstract
Depression is common, debilitating, and often chronic. Given that only 50% of patients
improve under initial treatment, prevention may be the most effective way to change
depression’s global disease burden. The biggest barrier to successful prevention is to
identify those at risk for depression in the near future. To close this gap, we are
carrying out the ERC-funded WARN-D study, an effort to build a personalized early
warning system for depression. In WARN-D, we follow 2000 students over 2 years,
using smartphones, smartwatches, systems science, and machine learning. Collected
data will be utilized to build a personalized prediction model for depression onset,
most likely as part of a smartphone app. Overall, WARN-D will function similar to a
weather forecast, with the core difference that one can only seek shelter from a
thunderstorm and clean up afterwards, while depression may be successfully
prevented before it occurs.

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