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Classification and discrimination of emotion dysregulation disorders using machine learning.

Ryan J Murray1, Ben Meuleman2, Eléonore Pham3

  • 1Synapsy Center, Department of Psychiatry, Faculty of Medicine, University of Geneva, Campus Biotech, Geneva, Switzerland.

Journal of Affective Disorders
|February 1, 2026
PubMed
Summary

Attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), and borderline personality disorder (BPD) are distinct conditions. Machine learning effectively differentiates these disorders using emotion regulation and environmental factors.

Keywords:
Attention-deficit/hyperactivity disorderBipolar disorderBorderline personality disorderEmotion dysregulationMachine learningOffspring

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Area of Science:

  • Psychiatry
  • Neuroscience
  • Machine Learning

Background:

  • Attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), and borderline personality disorder (BPD) are severe psychiatric conditions.
  • These disorders frequently exhibit overlapping symptoms of emotion dysregulation.
  • The potential for a continuum of emotion dysregulation across these disorders remains unclear.

Purpose of the Study:

  • To investigate whether ADHD, BD, and BPD exist on a continuum of emotion dysregulation.
  • To utilize machine learning (ML) to predict and discriminate between these disorders using psychological and environmental features.
  • To examine how offspring of individuals with these disorders relate to their parent disorders and healthy controls.

Main Methods:

  • Machine learning algorithms were applied to data from 232 adults, including 92 patients with ADHD, BD, or BPD, 67 genetically unrelated offspring, and 73 healthy controls.
  • Features analyzed included clinical dimensions of emotion dysregulation (e.g., impulsivity, mania, rumination), childhood trauma, and parental bonding.
  • Classification and prediction models were developed to differentiate between the diagnostic groups.

Main Results:

  • Borderline personality disorder (BPD) demonstrated strong discrimination from healthy controls and ADHD, and moderate discrimination from bipolar disorder (BD).
  • Attention-deficit/hyperactivity disorder (ADHD) showed strong discrimination from healthy controls and BPD, and moderate discrimination from BD.
  • Bipolar disorder (BD) exhibited moderate discrimination from ADHD and BPD, and weak discrimination from healthy controls.
  • Offspring groups more closely resembled healthy controls than their respective parent disorders.
  • Machine learning reliably classified ADHD, BD, and BPD as independent constructs based on emotion regulation and environmental factors.

Conclusions:

  • ADHD, BD, and BPD represent distinct psychiatric constructs, not a continuum of emotion dysregulation.
  • Machine learning, utilizing emotion regulation traits and environmental factors, offers a reliable method for classifying these disorders.
  • The findings suggest potential for cost-effective ML-based classification and prediction of these emotion dysregulation disorders.