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Predicting personalized process-outcome associations in psychotherapy using machine learning approaches-A

Julian A Rubel1, Sigal Zilcha-Mano2, Julia Giesemann1

  • 1Department of Psychology, University of Trier, Trier, Germany.

Psychotherapy Research : Journal of the Society for Psychotherapy Research
|March 28, 2019
PubMed
Summary

Personalized treatment methods were tested in process-outcome research. The study found that predicting alliance-outcome associations using similar patients did not improve prediction accuracy.

Keywords:
alliance-outcome researchlongitudinal datamoderators of alliance-outcome associationnearest neighborpersonalized mental healthwithin- and between-patients effects

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Area of Science:

  • Psychology
  • Medical Research

Background:

  • Personalized treatment shows promise in medicine and mental health.
  • Its utility in process-outcome research, specifically alliance-outcome association, is largely unknown.

Purpose of the Study:

  • To apply personalized treatment methods to process-outcome research.
  • To investigate the alliance-outcome association using personalized prediction models.

Main Methods:

  • Utilized data from 741 patients to estimate within-patient alliance-outcome effects.
  • Employed the Boruta algorithm to identify moderating patient characteristics.
  • Applied a nearest neighbor approach for personalized prediction of alliance-outcome associations.

Main Results:

  • The correlation between observed and predicted alliance-outcome associations was low and insignificant.
  • The personalized prediction model did not outperform a simple comparison model.

Conclusions:

  • This study demonstrated the application of personalized treatment methods in process-outcome research.
  • The findings suggest limitations in current personalized prediction approaches for alliance-outcome associations.
  • Opens avenues for future research in personalized process-outcome research.