用大模型整合非洲草药知识，打造个性化抑郁支持系统。
The Application of Large Language Models on Major Depressive Disorder Support Based on African Natural Products
- 结合非洲草药传统知识与DeepSeek大模型，构建智能抑郁症辅助系统。
- 系统可提供草药疗效、用法及安全信息，支持个性化干预方案。
- 适合对传统疗法和AI医疗融合感兴趣的临床或研究者参考。
重度抑郁症是21世纪最重要的全球健康挑战之一，影响全球数百万人并带来重大经济与社会负担。尽管常规抗抑郁疗法为许多人缓解症状，但其起效慢、副作用显著以及部分患者存在治疗抵抗等问题，促使研究人员探索替代疗法。非洲传统医学拥有数千年植物药疗经验，为开发新型抗抑郁药物提供了宝贵资源。本文探讨将大语言模型与非洲天然产物结合用于抑郁症支持，融合传统知识与现代人工智能技术，构建可访问、基于证据的心理健康支持系统。研究涵盖具有明确抗抑郁作用的非洲药用植物分析、其药理机制及一个基于DeepSeek大模型的AI支持系统开发。该系统提供非洲草药的循证信息，包括临床应用、安全性考量和治疗方案，同时保持科学严谨性与安全标准。研究结果表明，大语言模型可作为连接传统智慧与现代医疗的桥梁，提供个性化、文化适配的抑郁症支持，兼顾传统认知与现代医学理解。
原文摘要 · Abstract (English)
Major depressive disorder represents one of the most significant global health challenges of the 21st century, affecting millions of people worldwide and creating substantial economic and social burdens. While conventional antidepressant therapies have provided relief for many individuals, their limitations including delayed onset of action, significant side effects, and treatment resistance in a substantial portion of patients have prompted researchers and healthcare providers to explore alternative therapeutic approaches (Kasneci et al.). African traditional medicine, with its rich heritage of plant-based remedies developed over millennia, offers a valuable resource for developing novel antidepressant treatments that may address some of these limitations. This paper examines the integration of large language models with African natural products for depression support, combining traditional knowledge with modern artificial intelligence technology to create accessible, evidence-based mental health support systems. The research presented here encompasses a comprehensive analysis of African medicinal plants with documented antidepressant properties, their pharmacological mechanisms, and the development of an AI-powered support system that leverages DeepSeek's advanced language model capabilities. The system provides evidence-based information about African herbal medicines, their clinical applications, safety considerations, and therapeutic protocols while maintaining scientific rigor and appropriate safety standards. Our findings demonstrate the potential for large language models to serve as bridges between traditional knowledge and modern healthcare, offering personalized, culturally appropriate depression support that honors both traditional wisdom and contemporary medical understanding.
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