MHINDR -- a DSM5 based mental health diagnosis and recommendation framework using LLM
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Abstract (arXiv)
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.
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References · 10
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2020Event Extraction by Answering (Almost) Natural Questionscited by 177
- 2019Dreaddit: A Reddit Dataset for Stress Analysis in Social Mediacited by 81
- 2023Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Methodcited by 76
- 2023MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Modelscited by 63
- 2024A Computational Framework for Behavioral Assessment of LLM Therapistscited by 61
- 2022Data set creation and empirical analysis for detecting signs of depression from social media postingscited by 26
- 2022MentSum: A Resource for Exploring Summarization of Mental Health Online Postscited by 11
- 2024Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social mediacited by 7
- 2024Illuminate: A novel approach for depression detection with explainable analysis and proactive therapy using prompt engineeringcited by 4
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