Machine learning for antidepressant treatment selection in depression

Prehm I.M. Arnold*, Joost G.E. Janzing, Arjen Hommersom

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In this review, we summarize the current evidence of ML in the selection of antidepressants and conclude that its value for clinical practice is still limited. Apart from the current focus on effectiveness, several other factors should be taken into account to make ML-based prediction models useful for clinical application.
Original languageEnglish
Article number104068
Number of pages9
JournalDrug Discovery Today
Volume29
Issue number8
Early online date24 Jun 2024
DOIs
Publication statusE-pub ahead of print - 24 Jun 2024

Keywords

  • Major depressive disorder
  • machine learning
  • antidepressants
  • psychiatry
  • dynamic treatment regimes
  • treatment

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