Automated Multilabel Mpox Research Classification with Explainable Transformer Models

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Tanjim Taharat Aurpa
• Venue: arXiv: cs.CL
• Published: 2026-07-29
• Collected: 2026-07-30
• URL: http://arxiv.org/abs/2607.26700v1
• PDF: https://arxiv.org/pdf/2607.26700v1
• DOI: UNKNOWN

Plain-Language Brief

A study published in 2026 on arXiv focuses on using AI models, specifically BERT, to classify Mpox research articles into key topics such as outbreaks, vaccination, and epidemiology. BERT was found to perform well, achieving high accuracy and F1 scores, and SHAP was used to analyze significant word features and patterns.

What the Document Says

• The Mpox outbreak is a public health issue with increasing cases in some regions.
• Research on Mpox is important for vaccine development, diagnostic improvement, viral evolution studies, and preventing future outbreaks.
• The study uses multilabel classification to categorize 14590 Mpox research articles.
• Among the AI models tested, BERT achieved the best results with 97.05% accuracy, 97.67% micro F1 score, and 96.46% macro F1 score.
• SHAP was used to analyze significant word features and patterns in the decisions made by the model.

Key Concepts Mentioned

• Mpox
• research articles
• multilabel classification
• BERT
• SHAP

Claims Made by the Document

• The Mpox outbreak is a public health issue.
• Research on Mpox is important for several reasons.
• BERT performed the best among the AI models tested.

Evidence Used

• The text states that the Mpox outbreak is a serious public health issue.
• The text mentions that research on Mpox is vital for various reasons.
• The text reports that BERT achieved high accuracy and F1 scores.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.