Comorbid bipolar disorder and borderline personality disorder: Diagnosis using machine learning

Comorbid bipolar disorder (BP) and borderline personality disorder (BPD) presents a diagnostic challenge in its differentiation from each condition individually. We aimed to use a machine learning (ML) approach to differentiate comorbid BP/BPD from both BP and BPD. Participants were assigned DSM diagnoses and compared on self-report measures examining personality, emotion regulation strategies and perceived parental experiences during childhood. 82 participants were assigned as BP, 52 as BPD and 53 as comorbid BP/BPD. ML-derived diagnoses had an accuracy of 79.6% in classifying BP/BPD vs. BP, and 61.7% in classifying BP/BPD vs. BPD. Stress-related paranoid ideation and other core borderline personality items were important in distinguishing BP/BPD vs. BP, whereas deficits in emotion regulation strategies were important in distinguishing BP/BPD vs. BPD. Impulsivity and anger were important across both analyses. We identified clinical variables more distinctive in comorbid BP/BPD, with superior accuracy in distinguishing from BP, and with lower accuracy compared to BPD alone. Such an additive model should assist in sharpening clinical decision making, with future machine learning examination of larger datasets likely to further improve diagnostic accuracy.