CASE: Efficient Curricular Data Pre-training for Building Assistive Psychology Expert Models

• URL: http://arxiv.org/abs/2406.00314v3
• Date: Wed, 02 Oct 2024 17:44:46 GMT
• Title: CASE: Efficient Curricular Data Pre-training for Building Assistive Psychology Expert Models
• Authors: Sarthak Harne, Monjoy Narayan Choudhury, Madhav Rao, TK Srikanth, Seema Mehrotra, Apoorva Vashisht, Aarushi Basu, Manjit Sodhi,
• Abstract summary: This study explores the use of Natural Language Processing (NLP) pipelines to analyze text data from online mental health forums used for consultations. By analyzing forum posts, these pipelines can flag users who may require immediate professional attention. Case-BERT demonstrates superior performance compared to existing methods, achieving an f1 score of 0.91 for Depression and 0.88 for Anxiety.
• Score: 1.0840985826142429
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The limited availability of psychologists necessitates efficient identification of individuals requiring urgent mental healthcare. This study explores the use of Natural Language Processing (NLP) pipelines to analyze text data from online mental health forums used for consultations. By analyzing forum posts, these pipelines can flag users who may require immediate professional attention. A crucial challenge in this domain is data privacy and scarcity. To address this, we propose utilizing readily available curricular texts used in institutes specializing in mental health for pre-training the NLP pipelines. This helps us mimic the training process of a psychologist. Our work presents CASE-BERT that flags potential mental health disorders based on forum text. CASE-BERT demonstrates superior performance compared to existing methods, achieving an f1 score of 0.91 for Depression and 0.88 for Anxiety, two of the most commonly reported mental health disorders. Our code and data are publicly available.

Related papers

• Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice [2.9775344067885974]
  We propose a novel adaptive Retrieval-Augmented Generation (RAG) approach that completes psychological questionnaires by analyzing social media posts. Our method retrieves the most relevant user posts for each question in a psychological survey and uses Large Language Models (LLMs) to predict questionnaire scores in a zero-shot setting.
  arXiv  Detail & Related papers  (2025-01-02T00:01:54Z)
• ConvCounsel: A Conversational Dataset for Student Counseling [31.298840947078364]
  This paper introduces a specialized mental health dataset that emphasizes the active listening strategy employed in conversation for counseling, also named as ConvCounsel. To demonstrate the utility of the proposed dataset, this paper also presents the NYCUKA, a spoken mental health dialogue system that is designed by using the ConvCounsel dataset.
  arXiv  Detail & Related papers  (2024-11-01T14:08:02Z)
• 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)
• PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals [22.87612889868498]
  PATIENT-Psi is a novel patient simulation framework for cognitive behavior therapy (CBT) training. We propose an interactive training scheme, PATIENT-Psi-TRAINER, for mental health trainees to practice a key skill in CBT.
  arXiv  Detail & Related papers  (2024-05-30T03:20:56Z)
• PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection [50.66968526809069]
  We propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner. Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection.
  arXiv  Detail & Related papers  (2023-10-31T08:23:33Z)
• UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts [0.0]
  We propose UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensembling of BERTs for producing reliable and well-calibrated predictions. We analyze unstructured user data on Reddit to classify six possible types of mental illnesses- None, Depression, Anxiety, Bipolar Disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2023-04-10T12:18:53Z)
• Semantic Similarity Models for Depression Severity Estimation [53.72188878602294]
  This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We use test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. We evaluate our methods on two Reddit-based benchmarks, achieving 30% improvement over state of the art in terms of measuring depression severity.
  arXiv  Detail & Related papers  (2022-11-14T18:47:26Z)
• Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [55.653944436488786]
  According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD. This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2022-07-03T11:33:52Z)
• Benchmarking Differential Privacy and Federated Learning for BERT Models [0.6524460254566904]
  Depression is a serious medical illness that can have adverse effects on how one feels, thinks, and acts. Due to the sensitive nature of such data, privacy measures need to be taken for handling and training models with such data. We study the effects that the application of Differential Privacy (DP) has, in both a centralized and a Federated Learning (FL) setup, on training contextualized language models.
  arXiv  Detail & Related papers  (2021-06-26T08:52:02Z)
• Towards Preemptive Detection of Depression and Anxiety in Twitter [14.877991297466174]
  Depression and anxiety are psychiatric disorders that are observed in many areas of everyday life. We develop a dataset designed to foster research in depression and anxiety detection in Twitter.
  arXiv  Detail & Related papers  (2020-11-10T17:17:23Z)
• Anxiety Detection Leveraging Mobile Passive Sensing [53.11661460916551]
  Anxiety disorders are the most common class of psychiatric problems affecting both children and adults. Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods. eWellness is an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner.
  arXiv  Detail & Related papers  (2020-08-09T20:22:52Z)

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.