Computational Modeling and Analysis of Therapeutical Interventions for Depression

  • Fiemke Both
  • Mark Hoogendoorn
  • Michel C. A. Klein
  • Jan Treur
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6334)


Depressions impose a huge burden on both the patient suffering from a depression as well as society in general. In order to make interventions for a depressed patient during a therapy more personalized and effective, a supporting personal software agent can be useful. Such an agent should then have a good idea of the current state of the person. A computational model for human mood regulation and depression has been developed in previous work, but in order for the agent to give optimal support during an intervention, it should also have knowledge on the precise functioning of the intervention in relation with the mood regulation and depression. This paper therefore presents computational models for these interventions for different types of therapy. Simulation results are presented showing that the mood regulation and depression indeed follow the expected patterns when applying these therapies. The intervention models have been evaluated for a variety of patient types by simulation experiments and formal verification.


Cognitive Behavioral Therapy Coping Skill Negative Thought Person Type Activity Schedule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fiemke Both
    • 1
  • Mark Hoogendoorn
    • 1
  • Michel C. A. Klein
    • 1
  • Jan Treur
    • 1
  1. 1.Department of Artificial IntelligenceVU University AmsterdamAmsterdamThe Netherlands

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