Advancing Mental Health Pre-Screening: A New Custom GPT for Psychological Distress Assessment

• URL: http://arxiv.org/abs/2408.01614v1
• Date: Sat, 3 Aug 2024 00:38:30 GMT
• Title: Advancing Mental Health Pre-Screening: A New Custom GPT for Psychological Distress Assessment
• Authors: Jinwen Tang, Yi Shang,
• Abstract summary: 'Psycho Analyst' is a custom GPT model based on OpenAI's GPT-4, optimized for pre-screening mental health disorders. The model adeptly decodes nuanced linguistic indicators of mental health disorders.
• Score: 0.8287206589886881
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study introduces 'Psycho Analyst', a custom GPT model based on OpenAI's GPT-4, optimized for pre-screening mental health disorders. Enhanced with DSM-5, PHQ-8, detailed data descriptions, and extensive training data, the model adeptly decodes nuanced linguistic indicators of mental health disorders. It utilizes a dual-task framework that includes binary classification and a three-stage PHQ-8 score computation involving initial assessment, detailed breakdown, and independent assessment, showcasing refined analytic capabilities. Validation with the DAIC-WOZ dataset reveals F1 and Macro-F1 scores of 0.929 and 0.949, respectively, along with the lowest MAE and RMSE of 2.89 and 3.69 in PHQ-8 scoring. These results highlight the model's precision and transformative potential in enhancing public mental health support, improving accessibility, cost-effectiveness, and serving as a second opinion for professionals.

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)
• Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction [54.23208041792073]
  Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review. A key challenge in the ASQP task is the scarcity of labeled data, which limits the performance of existing methods. We propose a self-training framework with a pseudo-label scorer, wherein a scorer assesses the match between reviews and their pseudo-labels.
  arXiv  Detail & Related papers  (2024-06-26T05:30:21Z)
• Evaluating the Effectiveness of the Foundational Models for Q&A Classification in Mental Health care [0.18416014644193068]
  Pre-trained Language Models (PLMs) have the potential to transform mental health support. This study evaluates the effectiveness of PLMs for classification of Questions and Answers in the domain of mental health care.
  arXiv  Detail & Related papers  (2024-06-23T00:11:07Z)
• Comparing the Efficacy of GPT-4 and Chat-GPT in Mental Health Care: A Blind Assessment of Large Language Models for Psychological Support [0.0]
  Two large language models, GPT-4 and Chat-GPT, were tested in responding to a set of 18 psychological prompts. GPT-4 achieved an average rating of 8.29 out of 10, while Chat-GPT received an average rating of 6.52.
  arXiv  Detail & Related papers  (2024-05-15T12:44:54Z)
• Fine-tuning Large Language Models for Automated Diagnostic Screening Summaries [0.024105148723769353]
  We evaluate several state-of-the-art Large Language Models (LLMs) for generating concise summaries from mental state examinations. We rigorously evaluate four different models for summary generation using established ROUGE metrics and input from human evaluators. Our top-performing fine-tuned model outperforms existing models, achieving ROUGE-1 and ROUGE-L values of 0.810 and 0.764, respectively.
  arXiv  Detail & Related papers  (2024-03-29T12:25:37Z)
• Mental Health Diagnosis in the Digital Age: Harnessing Sentiment Analysis on Social Media Platforms upon Ultra-Sparse Feature Content [3.6195994708545016]
  We propose a novel semantic feature preprocessing technique with a three-folded structure. With enhanced semantic features, we train a machine learning model to predict and classify mental disorders. Our methods, when compared to seven benchmark models, demonstrate significant performance improvements.
  arXiv  Detail & Related papers  (2023-11-09T00:15:06Z)
• The Capability of Large Language Models to Measure Psychiatric Functioning [9.938814639951842]
  The Med-PaLM 2 is capable of assessing psychiatric functioning across a range of psychiatric conditions. The strongest performance was the prediction of depression scores based on standardized assessments. Results show the potential for general clinical language models to flexibly predict psychiatric risk.
  arXiv  Detail & Related papers  (2023-08-03T15:52:27Z)
• From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
  We discuss a paradigm shift from static evaluation methods to adaptive testing. This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time. We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• Evaluating Psychological Safety of Large Language Models [72.88260608425949]
  We designed unbiased prompts to evaluate the psychological safety of large language models (LLMs) We tested five different LLMs by using two personality tests: Short Dark Triad (SD-3) and Big Five Inventory (BFI) Despite being instruction fine-tuned with safety metrics to reduce toxicity, InstructGPT, GPT-3.5, and GPT-4 still showed dark personality patterns. Fine-tuning Llama-2-chat-7B with responses from BFI using direct preference optimization could effectively reduce the psychological toxicity of the model.
  arXiv  Detail & Related papers  (2022-12-20T18:45:07Z)
• Textual Data Augmentation for Patient Outcomes Prediction [67.72545656557858]
  We propose a novel data augmentation method to generate artificial clinical notes in patients' Electronic Health Records. We fine-tune the generative language model GPT-2 to synthesize labeled text with the original training data. We evaluate our method on the most common patient outcome, i.e., the 30-day readmission rate.
  arXiv  Detail & Related papers  (2022-11-13T01:07:23Z)
• IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction [47.999621481852266]
  We propose an interpretable graph learning-based model which interprets the clinical relevance of the input features towards the task. In a clinical scenario, such a model can assist the clinical experts in better decision-making for diagnosis and treatment planning. Our proposed model shows superior performance with respect to compared methods with an increase in an average accuracy of 3.2% for Tadpole, 1.6% for UKBB Gender, and 2% for the UKBB Age prediction task.
  arXiv  Detail & Related papers  (2021-03-29T13:04:02Z)

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.