  Online mental health treatment has the premise to meet the increasing demand
for mental health treatment at a lower cost than traditional treatment.
However, online treatment suffers from high drop-out rates, which might negate
their cost effectiveness. Predictive models might aid in early identification
of deviating clients which allows to target them directly to prevent drop-out
and improve treatment outcomes. We propose a two-staged multi-objective
optimization process to automatically infer model structures based on
ecological momentary assessment for prediction of future symptom development.
The proposed multi-objective optimization approach results in a temporal-causal
network model with the best prediction performance for each concept. This
allows for a selection of a disorder-specific model structure based on the
envisioned field of application.
