Differential diagnosis of borderline personality disorder: machine learning models of subjective experiences

Research output: Working paperPreprint

Abstract

The present study aimed to capture the subjective experiences that distinguish BPD from closely related mental disorders to aid differential diagnosis. Posts from Reddit were downloaded from seven mental health discussion groups. The topics discussed in each post were extracted using a combination of machine learning approaches. Logistic regression models were used to classify whether posts originated from the BPD support group versus other support groups. The average classification performance was well above chance, even relying on only 25 subjective experiences, and after excluding topics that were objective markers of diagnoses (e.g., names of the disorders or medications). Fear of abandonment emerged as the key differentiator of BPD, and 11 of the 25 topics related directly to the definition of BPD in the DSM-5. However, several of these were typical of other disorders as well, raising questions about their diagnostic utility.
Original languageEnglish
DOIs
Publication statusSubmitted - 17 Mar 2023

Publication series

NamePsyArXiv

Fingerprint

Dive into the research topics of 'Differential diagnosis of borderline personality disorder: machine learning models of subjective experiences'. Together they form a unique fingerprint.

Cite this