发表机构
University of Frontier Technology（前沿技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出用BERT结合SHAP的可解释Transformer模型，对14590篇猴痘研究文献做多标签分类，准确率达97.05%，可辅助相关人员高效获取信息。
AI 中文摘要
猴痘疫情仍是严重的公共卫生问题，世界卫生组织（WHO）报告部分地区病例数持续上升。猴痘研究对疫苗研发、诊断改进、病毒演化研究及预防未来疫情至关重要，但大量发表的研究使信息组织与高效分析困难。本研究采用多标签分类方法，将14590篇猴痘研究文献归类为疫情、疫苗接种、流行病学等关键主题。在测试的不同AI模型中，BERT表现最优，准确率达97.05%，微F1值97.67%，宏F1值96.46%。为明确模型决策逻辑，使用SHAP分析重要词特征与模式。结果表明，BERT可实现猴痘研究自动分类，助力研究人员、政策制定者及医护人员快速获取相关信息，节省时间并提升公共卫生工作成效。
英文摘要
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.
