Predicting remission after acute phase pharmacotherapy in patients with bipolar I depression: A machine learning approach with cross-trial and cross-drug replication.

Journal: Bipolar disorders
PMID: 39362832

Abstract

OBJECTIVES: Interpatient variability in bipolar I depression (BP-D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine learning workflow was developed to predict remission after 8 weeks of pharmacotherapy (total score of ≤8 on the Montgomery Åsberg Depression Rating Scale [MADRS]).

Authors

  • Jean Marrero-Polanco
    Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA.
  • Jeremiah B Joyce
    Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA.
  • Caroline W Grant
    Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA.
  • Paul E Croarkin
    Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA.
  • Arjun P Athreya
    Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
  • William V Bobo
    Department of Psychiatry & Psychology, Mayo Clinic, Jacksonville, FL 32224, USA.

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