用AI从社交媒体识别药物滥用与过量症状，准确率超97%。
Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media
- 结合大模型与人工标注，构建多类多标签检测框架。
- 多分类准确率达98%，多标签达97%，优于基线模型8%。
- 适合公共卫生监测与个性化干预研究者使用。
药物过量仍是全球重大健康问题，常由阿片类、止痛药及精神科药物滥用引发。传统研究方法受限，而社交媒体可提供用药及过量症状的实时自述数据。本研究提出一种基于AI的NLP框架，利用标注后的社交媒体数据，检测常用药物及其相关过量症状。采用大模型与人工结合的混合标注策略，应用传统机器学习、神经网络及先进的Transformer模型。该框架在多分类任务中达到98%准确率，在多标签分类中达97%，较基线模型提升最高8%。结果表明，AI在支持公共卫生监测和个性化干预策略方面具有巨大潜力。
原文摘要 · Abstract (English)
Drug overdose remains a critical global health issue, often driven by misuse of opioids, painkillers, and psychiatric medications. Traditional research methods face limitations, whereas social media offers real-time insights into self-reported substance use and overdose symptoms. This study proposes an AI-driven NLP framework trained on annotated social media data to detect commonly used drugs and associated overdose symptoms. Using a hybrid annotation strategy with LLMs and human annotators, we applied traditional ML models, neural networks, and advanced transformer-based models. Our framework achieved 98% accuracy in multi-class and 97% in multi-label classification, outperforming baseline models by up to 8%. These findings highlight the potential of AI for supporting public health surveillance and personalized intervention strategies.
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