Research Trend Visualization by MeSH Terms from PubMed

Abstract

Motivation: PubMed is a primary source of biomedical information comprising search tool function and the biomedical literature from MEDLINE which is the US National Library of Medicine premier bibliographic database, life science journals and online books. Complimentary tools to PubMed have been developed to help the users search for literature and acquire knowledge. However, these tools are insufficient to overcome the difficulties of the users due to the proliferation of biomedical literature. A new method is needed for searching the knowledge in biomedical field. Methods: A new method is proposed in this study for visualizing the recent research trends based on the retrieved documents corresponding to a search query given by the user. The Medical Subject Headings (MeSH) are used as the primary analytical element. MeSH terms are extracted from the literature and the correlations between them are calculated. A MeSH network, called MeSH Net, is generated as the final result based on the Pathfinder Network algorithm. Results: A case study for the verification of proposed method was carried out on a research area defined by the search query (immunotherapy and cancer and "tumor microenvironment"). The MeSH Net generated by the method is in good agreement with the actual research activities in the research area (immunotherapy). Conclusion: A prototype application generating MeSH Net was developed. The application, which could be used as a "guide map for travelers", allows the users to quickly and easily acquire the knowledge of research trends. Combination of PubMed and MeSH Net is expected to be an effective complementary system for the researchers in biomedical field experiencing difficulties with search and information analysis.

Keywords: MeSH Net; MeSH correlations; PubMed; keyword network; medical subject headings.

Figure 1

Flowchart for MeSH term dataset preparation.

Figure 2

The pseudo-code for measurement of the Medical Subject Headings (MeSH) term correlations and the adoption of Pathfinder Network (PFNET) algorithm.

Figure 3

Medical Subject Headings (MeSH) network based on the MeSH term correlation matrix prepared from the MeSH term co-occurrence count for the case study research area. The colors of the nodes represent different characteristics of MeSH terms: red dots for Disease, green for Chemicals and Drugs, and blue for other MeSH terms.

Figure 4

Medical Subject Headings (MeSH) network based on the MeSH term correlation matrix prepared from the title similarities for the case study research area. This network is referred to as “the MeSH Net”. The six sub-areas in dotted lines are in good agreement with the actual research activities in as confirmed by an expert in the corresponding research area. The representative MeSH terms for each sub-area are shown marked with blue shades. The colors of the nodes are the same as those in Figure 3.