Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews
This work addresses a diagnostic problem for clinicians dealing with overlapping psychiatric disorders, but it is incremental as it builds on existing multimodal detection methods with a new dataset.
The paper tackled the challenge of distinguishing bipolar disorder (BD) and borderline personality disorder (BPD) using language and speech from non-clinical interviews, resulting in a good linear classifier with a down-selected feature set that provides insights into differences between the conditions.
Bipolar disorder (BD) and borderline personality disorder (BPD) are both chronic psychiatric disorders. However, their overlapping symptoms and common comorbidity make it challenging for the clinicians to distinguish the two conditions on the basis of a clinical interview. In this work, we first present a new multi-modal dataset containing interviews involving individuals with BD or BPD being interviewed about a non-clinical topic . We investigate the automatic detection of the two conditions, and demonstrate a good linear classifier that can be learnt using a down-selected set of features from the different aspects of the interviews and a novel approach of summarising these features. Finally, we find that different sets of features characterise BD and BPD, thus providing insights into the difference between the automatic screening of the two conditions.