MHINDR -- a DSM5 based mental health diagnosis and recommendation framework using LLM
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915524694245376 |
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| author | Agarwal, Vaishali Thukral, Sachin Chatterjee, Arnab |
| author_facet | Agarwal, Vaishali Thukral, Sachin Chatterjee, Arnab |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25992 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MHINDR -- a DSM5 based mental health diagnosis and recommendation framework using LLM Agarwal, Vaishali Thukral, Sachin Chatterjee, Arnab Social and Information Networks Artificial Intelligence Information Retrieval 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. |
| title | MHINDR -- a DSM5 based mental health diagnosis and recommendation framework using LLM |
| topic | Social and Information Networks Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2509.25992 |