Mapping Research Trends of Medications for Multidrug-Resistant Pulmonary Tuberculosis Based on the Co-Occurrence of Specific Semantic Types in the MeSH Tree: A Bibliometric and Visualization-Based Analysis of PubMed Literature (1966–2020)

Shuang Xu,1 Yi Fu,2 Dan Xu,1 Shuang Han,1 Mingzhi Wu,3 Xinrong Ju,1 Meng Liu,1 De-Sheng Huang,4,5 Peng Guan4,6 1Library of China Medical University, Shenyang, Liaoning, People’s Republic of China; 2School of Health Management, China Medical University, Shenyang, Liaoning, People’s Republic of China; 3Library of Shenyang Pharmaceutical University, Shenyang, Liaoning, People’s Republic of China; 4Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, Shenyang, Liaoning, People’s Republic of China; 5Department of Intelligent Computing, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, People’s Republic of China; 6Department of Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning, People’s Republic of ChinaCorrespondence: De-Sheng Huang; Peng Guan, China Medical University, Shenyang, Liaoning, 110122, People’s Republic of China, Email dshuang@cmu.edu.cn; pguan@cmu.edu.cnBackground: Before the COVID-19 pandemic, tuberculosis is the leading cause of death from a single infectious agent worldwide for the past 30 years. Progress in the control of tuberculosis has been undermined by the emergence of multidrug-resistant tuberculosis. The aim of the study is to reveal the trends of research on medications for multidrug-resistant pulmonary tuberculosis (MDR-PTB) through a novel method of bibliometrics that co-occurs specific semantic Medical Subject Headings (MeSH).Methods: PubMed was used to identify the original publications related to medications for MDR-PTB. An R package for text mining of PubMed, pubMR, was adopted to extract data and construct the co-occurrence matrix-specific semantic types. Biclustering analysis of high-frequency MeSH term co-occurrence matrix was performed by gCLUTO. Scientific knowledge maps were constructed by VOSviewer to create overlay visualization and density visualization. Burst detection was performed by CiteSpace to identify the future research hotspots.Results: Two hundred and eight substances (chemical, drug, protein) and 147 diseases related to MDR-PTB were extracted to form a specific semantic co-occurrence matrix. MeSH terms with frequency greater than or equal to six were selected to construct high-frequency co-occurrence matrix (42 × 20) of specific semantic types contains 42 substances and 20 diseases. Biclustering analysis divided the medications for MDR-PTB into five clusters and reflected the characteristics of drug composition. The overlay map indicated the average age gradients of 42 high-frequency drugs. Fifteen top keywords and 37 top terms with the strongest citation bursts were detected.Conclusion: This study evaluated the literatures related to MDR-PTB drug therapy, providing a co-occurrence matrix model based on the specific semantic types and a new attempt for text knowledge mining. Compared with the macro knowledge structure or hot spot analysis, this method may have a wider scope of application and a more in-depth degree of analysis.Keywords: multidrug-resistant tuberculosis, pulmonary tuberculosis, medication trends, specific semantic types, MeSH tree, pubMR