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This study employs multilabel classification to organize 14,590 Mpox research articles into critical topics such as outbreaks, vaccination, and epidemiology, addressing the challenge of efficiently analyzing a growing body of literature. BERT emerged as the most effective model, achieving a remarkable 97.05% accuracy alongside high micro and macro F1 scores, demonstrating its capability in this domain. By utilizing SHAP for interpretability, the research not only automates classification but also enhances understanding of the model's decision-making process, ultimately aiding public health initiatives.
BERT's classification accuracy of 97.05% could revolutionize how researchers and policymakers access vital Mpox information amidst an overwhelming volume of literature.
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.