用AI自动分类猴痘研究文献，提升科研效率。
Automated Multilabel Mpox Research Classification with Explainable Transformer Models
- 基于BERT的多标签分类模型，精准识别猴痘研究主题
- 最高达97.67%微F1，对复杂文献分类效果出色
- 结合SHAP解释特征，让决策过程透明可信
猴痘疫情仍是重大公共卫生问题，世卫组织报告部分区域病例持续上升。相关研究对疫苗研发、诊断优化、病毒演化分析及未来防控至关重要。然而大量论文使信息组织与分析难度增加。本研究采用多标签分类方法，将14590篇猴痘研究文献自动归类至暴发、疫苗、流行病学等关键主题。在对比多种AI模型后，BERT表现最佳，准确率达97.05%，微平均F1为97.67%，宏平均F1为96.46%。通过SHAP分析模型决策中的关键词特征与模式，增强可解释性。结果表明，BERT可有效自动化猴痘研究文献分类，帮助研究人员、政策制定者和医疗工作者快速获取相关信息，节省时间并提升公共卫生响应效率。
原文摘要 · Abstract (English)
The Mpox outbreak remains a serious public health issue, with the WHO (World Health Organization) reporting increasing cases in some regions. Research on Mpox is vital for several reasons, including vaccine development, diagnostic improvement, viral evolution studies, and preventing future outbreaks. However, the large amount of research being published makes it difficult to organize and analyze information efficiently. This study focuses on using multilabel classification to categorize 14590 Mpox research articles into key topics such as outbreaks, vaccination, and epidemiology. Among the different AI models tested, BERT performed the best, achieving 97.05% accuracy, 97.67% micro F1 score, and 96.46% macro F1 score. To better understand how the model makes decisions, SHAP was used to analyze significant word features and patterns. The results show that BERT can help automate the classification of Mpox research, making it easier for researchers, policymakers, and healthcare workers to quickly find relevant information, saving time and improving public health efforts.
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