Using LLMs to Aid Annotation and Collection of Clinically-Enriched Data in Bipolar Disorder and Schizophrenia

• URL: http://arxiv.org/abs/2406.12687v1
• Date: Tue, 18 Jun 2024 15:00:24 GMT
• Title: Using LLMs to Aid Annotation and Collection of Clinically-Enriched Data in Bipolar Disorder and Schizophrenia
• Authors: Ankit Aich, Avery Quynh, Pamela Osseyi, Amy Pinkham, Philip Harvey, Brenda Curtis, Colin Depp, Natalie Parde,
• Abstract summary: This paper demonstrates the application of contemporary language models in sequence-to-sequence tasks to enhance mental health research. We show that small models are capable of annotation for domain-specific clinical variables, data collection for mental-health instruments, and perform better then commercial large models.
• Score: 9.804382916824245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: NLP in mental health has been primarily social media focused. Real world practitioners also have high case loads and often domain specific variables, of which modern LLMs lack context. We take a dataset made by recruiting 644 participants, including individuals diagnosed with Bipolar Disorder (BD), Schizophrenia (SZ), and Healthy Controls (HC). Participants undertook tasks derived from a standardized mental health instrument, and the resulting data were transcribed and annotated by experts across five clinical variables. This paper demonstrates the application of contemporary language models in sequence-to-sequence tasks to enhance mental health research. Specifically, we illustrate how these models can facilitate the deployment of mental health instruments, data collection, and data annotation with high accuracy and scalability. We show that small models are capable of annotation for domain-specific clinical variables, data collection for mental-health instruments, and perform better then commercial large models.

Related papers

• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder. Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment. We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews. Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• Automated Multi-Label Annotation for Mental Health Illnesses Using Large Language Models [0.9913418444556487]
  Mental health disorders, such as depression and Anxiety, often co-occur. Social media datasets typically focus on single-disorder labels. This paper proposes a novel methodology for cleaning, sampling, labeling, and combining data to create versatile multi-label datasets.
  arXiv  Detail & Related papers  (2024-12-05T01:33:03Z)
• MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media [31.752563319585196]
  Black box models are inflexible when switching between tasks, and their results typically lack explanations. With the rise of large language models (LLMs), their flexibility has introduced new approaches to the field. In this paper, we introduce the first multi-task Chinese Social Media Interpretable Mental Health Instructions dataset, consisting of 9K samples. We also propose MentalGLM series models, the first open-source LLMs designed for explainable mental health analysis targeting Chinese social media.
  arXiv  Detail & Related papers  (2024-10-14T09:29:27Z)
• 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)
• Towards Evaluating and Building Versatile Large Language Models for Medicine [57.49547766838095]
  We present MedS-Bench, a benchmark designed to evaluate the performance of large language models (LLMs) in clinical contexts. MedS-Bench spans 11 high-level clinical tasks, including clinical report summarization, treatment recommendations, diagnosis, named entity recognition, and medical concept explanation. MedS-Ins comprises 58 medically oriented language corpora, totaling 13.5 million samples across 122 tasks.
  arXiv  Detail & Related papers  (2024-08-22T17:01:34Z)
• 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)
• From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models [21.427976533706737]
  We take a novel approach that leverages large language models to synthesize clinically useful insights from multi-sensor data. We develop chain of thought prompting methods that use LLMs to generate reasoning about how trends in data relate to conditions like depression and anxiety. We find models like GPT-4 correctly reference numerical data 75% of the time, and clinician participants express strong interest in using this approach to interpret self-tracking data.
  arXiv  Detail & Related papers  (2023-11-21T23:53:27Z)
• 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)

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.