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

• 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)
• Large Language Model for Mental Health: A Systematic Review [2.9429776664692526]
  Large language models (LLMs) have attracted significant attention for potential applications in digital health. This systematic review focuses on their strengths and limitations in early screening, digital interventions, and clinical applications.
  arXiv  Detail & Related papers  (2024-02-19T17:58:41Z)
• From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models [22.140953523632724]
  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)
• Explainable Depression Symptom Detection in Social Media [2.433983268807517]
  We propose using transformer-based architectures to detect and explain the appearance of depressive symptom markers in the users' writings. Our natural language explanations enable clinicians to interpret the models' decisions based on validated symptoms.
  arXiv  Detail & Related papers  (2023-10-20T17:05:27Z)
• Editing Personality for Large Language Models [73.59001811199823]
  This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs) We construct a new benchmark dataset PersonalityEdit to address this task.
  arXiv  Detail & Related papers  (2023-10-03T16:02:36Z)
• 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)
• RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis [12.180396034315807]
  Schizophrenia is a severe mental health condition that requires a long and complicated diagnostic process. Past attempts to use deep learning for schizophrenia diagnosis from brain-imaging data have shown promise but suffer from a large training-application gap. We propose to reduce this training-application gap by focusing on readily accessible data.
  arXiv  Detail & Related papers  (2022-03-31T15:01:35Z)
• VBridge: Connecting the Dots Between Features, Explanations, and Data for Healthcare Models [85.4333256782337]
  VBridge is a visual analytics tool that seamlessly incorporates machine learning explanations into clinicians' decision-making workflow. We identified three key challenges, including clinicians' unfamiliarity with ML features, lack of contextual information, and the need for cohort-level evidence. We demonstrated the effectiveness of VBridge through two case studies and expert interviews with four clinicians.
  arXiv  Detail & Related papers  (2021-08-04T17:34:13Z)
• Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data [74.60507696087966]
  Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. One promising data source to help monitor human behavior is daily smartphone usage. We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
  arXiv  Detail & Related papers  (2021-06-24T17:46:03Z)
• Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks [62.9447303059342]
  We show the importance of this problem in medical community. We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence. We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
  arXiv  Detail & Related papers  (2020-07-15T09:22:55Z)

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.