Predicting therapy success for treatment as usual and blended treatment in the domain of depression

Ward van Breda, Vincent Bremer, Dennis Becker, Mark Hoogendoorn, Burkhardt Funk, Jeroen Ruwaard, Heleen Riper

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.
Original languageEnglish
Pages (from-to)100-104
Number of pages5
JournalInternet Interventions
Volume12
DOIs
Publication statusPublished - 2017

Keywords

  • Classification
  • Depression
  • E-health
  • Prediction
  • Therapy success

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