Intelligent Depression Prevention via LLM-Based Dialogue Analysis: Overcoming the Limitations of Scale-Dependent Diagnosis through Precise Emotional Pattern Recognition
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916703450955776 |
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| author | Zhong, Zhenguang Wang, Zhixuan |
| author_facet | Zhong, Zhenguang Wang, Zhixuan |
| contents | Existing depression screening predominantly relies on standardized questionnaires (e.g., PHQ-9, BDI), which suffer from high misdiagnosis rates (18-34% in clinical studies) due to their static, symptom-counting nature and susceptibility to patient recall bias. This paper presents an AI-powered depression prevention system that leverages large language models (LLMs) to analyze real-time conversational cues--including subtle emotional expressions (e.g., micro-sentiment shifts, self-referential language patterns)--for more accurate and dynamic mental state assessment. Our system achieves three key innovations: (1) Continuous monitoring through natural dialogue, detecting depression-indicative linguistic features (anhedonia markers, hopelessness semantics) with 89% precision (vs. 72% for PHQ-9); (2) Adaptive risk stratification that updates severity levels based on conversational context, reducing false positives by 41% compared to scale-based thresholds; and (3) Personalized intervention strategies tailored to users' emotional granularity, demonstrating 2.3x higher adherence rates than generic advice. Clinical validation with 450 participants shows the system identifies 92% of at-risk cases missed by traditional scales, while its explainable AI interface bridges the gap between automated analysis and clinician judgment. This work establishes conversational AI as a paradigm shift from episodic scale-dependent diagnosis to continuous, emotionally intelligent mental health monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16504 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intelligent Depression Prevention via LLM-Based Dialogue Analysis: Overcoming the Limitations of Scale-Dependent Diagnosis through Precise Emotional Pattern Recognition Zhong, Zhenguang Wang, Zhixuan Neurons and Cognition Human-Computer Interaction Existing depression screening predominantly relies on standardized questionnaires (e.g., PHQ-9, BDI), which suffer from high misdiagnosis rates (18-34% in clinical studies) due to their static, symptom-counting nature and susceptibility to patient recall bias. This paper presents an AI-powered depression prevention system that leverages large language models (LLMs) to analyze real-time conversational cues--including subtle emotional expressions (e.g., micro-sentiment shifts, self-referential language patterns)--for more accurate and dynamic mental state assessment. Our system achieves three key innovations: (1) Continuous monitoring through natural dialogue, detecting depression-indicative linguistic features (anhedonia markers, hopelessness semantics) with 89% precision (vs. 72% for PHQ-9); (2) Adaptive risk stratification that updates severity levels based on conversational context, reducing false positives by 41% compared to scale-based thresholds; and (3) Personalized intervention strategies tailored to users' emotional granularity, demonstrating 2.3x higher adherence rates than generic advice. Clinical validation with 450 participants shows the system identifies 92% of at-risk cases missed by traditional scales, while its explainable AI interface bridges the gap between automated analysis and clinician judgment. This work establishes conversational AI as a paradigm shift from episodic scale-dependent diagnosis to continuous, emotionally intelligent mental health monitoring. |
| title | Intelligent Depression Prevention via LLM-Based Dialogue Analysis: Overcoming the Limitations of Scale-Dependent Diagnosis through Precise Emotional Pattern Recognition |
| topic | Neurons and Cognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.16504 |