WP 4 Personalised Depression Treatment Modelling

WP leader: Prof. dr. Burkhardt Funk, ULG


The aim of this WP is to provide an innovative computerized method that contributes to the assessment of the impact of internet-based blended treatments compared to TAU on an individual patient level. Data collected in WP 1 and WP 2 will be used to develop dynamic models that predict individual depression trajectories and effectiveness of different types of therapies.

Specific Objectives

  • Development of dynamic models based on the state-of-the-art in depression treatment as collected in WP1
  • Development of a distributed infrastructure to improve models based on data that will be collected in WP2
  • Application of machine learning techniques for the improvement of predictive models
  • Identification of patient clusters to assess the effectiveness of treatment types