MHINDR - a DSM5 based mental health diagnosis and recommendation framework using LLM

• URL: http://arxiv.org/abs/2509.25992v1
• Date: Tue, 30 Sep 2025 09:26:38 GMT
• Title: MHINDR - a DSM5 based mental health diagnosis and recommendation framework using LLM
• Authors: Vaishali Agarwal, Sachin Thukral, Arnab Chatterjee,
• Abstract summary: Mental health forums offer valuable insights into psychological issues, stressors, and potential solutions.<n>We propose MHINDR, a large language model (LLM) based framework integrated with DSM-5 criteria to analyze user-generated text, dignose mental health conditions, and generate personalized interventions and insights for mental health practitioners.
• Score: 0.17842332554022688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mental health forums offer valuable insights into psychological issues, stressors, and potential solutions. We propose MHINDR, a large language model (LLM) based framework integrated with DSM-5 criteria to analyze user-generated text, dignose mental health conditions, and generate personalized interventions and insights for mental health practitioners. Our approach emphasizes on the extraction of temporal information for accurate diagnosis and symptom progression tracking, together with psychological features to create comprehensive mental health summaries of users. The framework delivers scalable, customizable, and data-driven therapeutic recommendations, adaptable to diverse clinical contexts, patient needs, and workplace well-being programs.

Related papers

• Responsible Evaluation of AI for Mental Health [72.85175110624736]
  Current approaches to evaluating AI tools in mental health care are fragmented and poorly aligned with clinical practice, social context, and first-hand user experience.<n>This paper argues for a rethinking of responsible evaluation by introducing an interdisciplinary framework that integrates clinical soundness, social context, and equity.
  arXiv  Detail & Related papers  (2026-01-20T12:55:10Z)
• A Comprehensive Review of Datasets for Clinical Mental Health AI Systems [55.67299586253951]
  We present the first comprehensive survey of clinical mental health datasets relevant to the training and development of AI-powered clinical assistants.<n>Our survey identifies critical gaps such as a lack of longitudinal data, limited cultural and linguistic representation, inconsistent collection and annotation standards, and a lack of modalities in synthetic data.
  arXiv  Detail & Related papers  (2025-08-13T13:42:35Z)
• MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis [58.67342568632529]
  MoodAngels is the first specialized multi-agent framework for mood disorder diagnosis.<n>MoodSyn is an open-source dataset of 1,173 synthetic psychiatric cases.
  arXiv  Detail & Related papers  (2025-06-04T09:18:25Z)
• Reasoning Is Not All You Need: Examining LLMs for Multi-Turn Mental Health Conversations [13.064927179032756]
  We introduce MedAgent, a novel framework for synthetically generating realistic, multi-turn mental health sensemaking conversations.<n>We present MultiSenseEval, a holistic framework to evaluate the multi-turn conversation abilities of LLMs in healthcare settings.
  arXiv  Detail & Related papers  (2025-05-26T16:42:02Z)
• Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling [17.809187205107232]
  PsyLLM is a large language model designed to integrate diagnostic and therapeutic reasoning for mental health counseling.<n>This pipeline processes real-world mental health posts and generates multi-turn dialogue structures.<n> Rigorous multi-dimensional filtering ensures the generation of high-quality, clinically aligned dialogue data.
  arXiv  Detail & Related papers  (2025-05-21T16:24:49Z)
• MAGI: Multi-Agent Guided Interview for Psychiatric Assessment [50.6150986786028]
  We present MAGI, the first framework that transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational navigation.<n>We show that MAGI advances LLM- assisted mental health assessment by combining clinical rigor, conversational adaptability, and explainable reasoning.
  arXiv  Detail & Related papers  (2025-04-25T11:08:27Z)
• Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions [0.5249805590164902]
  We propose a comprehensive framework that leverages Natural Language Processing (NLP) and Generative AI techniques to identify and assess mental health disorders.<n>We use rule-based labeling methods as well as advanced pre-trained NLP models to extract nuanced semantic features from the data.<n>We fine-tune domain-adapted and generic pre-trained NLP models based on predictions from specialized Large Language Models (LLMs) to improve classification accuracy.
  arXiv  Detail & Related papers  (2025-03-03T11:48:01Z)
• 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.<n>Privacy concerns limit the accessibility of personalized treatment data.<n>MentalArena is a self-play framework to train language models.
  arXiv  Detail & Related papers  (2024-10-09T13:06:40Z)
• MentalAgora: A Gateway to Advanced Personalized Care in Mental Health through Multi-Agent Debating and Attribute Control [40.21489535255022]
  MentalAgora is a novel framework employing large language models enhanced by interaction between multiple agents for tailored mental health support. This framework operates through three stages: strategic debating, tailored counselor creation, and response generation. Our evaluations, including experiments and user studies, demonstrate that MentalAgora aligns with professional standards and effectively meets user preferences.
  arXiv  Detail & Related papers  (2024-07-03T01:19:38Z)
• 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)
• MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment [1.696974372855528]
  Mental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history. We propose a personalized mental health tracking and mood prediction system that utilizes patient physiological data collected through personal health devices.
  arXiv  Detail & Related papers  (2023-07-09T22:30:47Z)

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.