Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews

• URL: http://arxiv.org/abs/2008.03408v2
• Date: Mon, 31 May 2021 04:23:15 GMT
• Title: Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews
• Authors: Bo Wang, Yue Wu, Niall Taylor, Terry Lyons, Maria Liakata, Alejo J Nevado-Holgado, Kate E A Saunders
• Abstract summary: Bipolar disorder (BD) and borderline personality disorder (BPD) are both chronic psychiatric disorders. Their overlapping symptoms and common comorbidity make it challenging for the clinicians to distinguish the two conditions on the basis of a clinical interview. We first present a new multi-modal dataset containing interviews involving individuals with BD or BPD being interviewed about a non-clinical topic.
• Score: 18.909983168436945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
  Mental health disorders are one of the most serious diseases in the world. Privacy concerns limit the accessibility of personalized treatment data. MentalArena is a self-play framework to train language models.
  arXiv  Detail & Related papers  (2024-10-09T13:06:40Z)
• LLM Questionnaire Completion for Automatic Psychiatric Assessment [49.1574468325115]
  We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C)
  arXiv  Detail & Related papers  (2024-06-09T09:03:11Z)
• Selfsupervised learning for pathological speech detection [0.0]
  Speech production is susceptible to influence and disruption by various neurodegenerative pathological speech disorders. These disorders lead to pathological speech characterized by abnormal speech patterns and imprecise articulation. Unlike neurotypical speakers, patients with speech pathologies or impairments are unable to access various virtual assistants such as Alexa, Siri, etc.
  arXiv  Detail & Related papers  (2024-05-16T07:12:47Z)
• Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
  Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
  arXiv  Detail & Related papers  (2023-02-05T22:33:49Z)
• GDPR Compliant Collection of Therapist-Patient-Dialogues [48.091760741427656]
  We elaborate on the challenges we faced in starting our collection of therapist-patient dialogues in a psychiatry clinic under the General Data Privacy Regulation of the European Union. We give an overview of each step in our procedure and point out the potential pitfalls to motivate further research in this field.
  arXiv  Detail & Related papers  (2022-11-22T15:51:10Z)
• D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat [25.852922703368133]
  In a depression-diagnosis-directed clinical session, doctors initiate a conversation with ample emotional support that guides the patients to expose their symptoms. Due to the social stigma associated with mental illness, the dialogue data related to depression consultation and diagnosis are rarely disclosed. We construct a Chinese dialogue dataset for Depression-Diagnosis-Oriented Chat which simulates the dialogue between doctors and patients during the diagnosis of depression.
  arXiv  Detail & Related papers  (2022-05-24T03:54:22Z)
• A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets [70.32630628211803]
  We propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. A new large medical dialogue dataset with multi-level fine-grained annotations is introduced. We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies.
  arXiv  Detail & Related papers  (2022-04-19T16:43:21Z)
• A Multimodal Approach for Automatic Mania Assessment in Bipolar Disorder [0.0]
  We create a multimodal decision system based on recordings of the patient in acoustic, linguistic, and visual modalities. We achieve a 64.8% unweighted average recall score, which improves the state-of-the-art performance achieved on this dataset.
  arXiv  Detail & Related papers  (2021-12-17T12:09:01Z)
• Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder [14.766941144375146]
  We propose a new approach of modelling short-term features with visibility-signature transform. We show the role of different sets of features in characterising BD and BPD.
  arXiv  Detail & Related papers  (2021-02-18T20:47:03Z)
• Towards Causality-Aware Inferring: A Sequential Discriminative Approach for Medical Diagnosis [142.90770786804507]
  Medical diagnosis assistant (MDA) aims to build an interactive diagnostic agent to sequentially inquire about symptoms for discriminating diseases. This work attempts to address these critical issues in MDA by taking advantage of the causal diagram. We propose a propensity-based patient simulator to effectively answer unrecorded inquiry by drawing knowledge from the other records.
  arXiv  Detail & Related papers  (2020-03-14T02:05:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.