Mapping scientific landscapes in UMLS research: a scientometric review

Abstract

Objective: The Unified Medical Language System (UMLS) is 1 of the most successful, collaborative efforts of terminology resource development in biomedicine. The present study aims to 1) survey historical footprints, emerging technologies, and the existing challenges in the use of UMLS resources and tools, and 2) present potential future directions.

Materials and methods: We collected 10 469 bibliographic records published between 1986 and 2019, using a Web of Science database. graph analysis, data visualization, and text mining to analyze domain-level citations, subject categories, keyword co-occurrence and bursts, document co-citation networks, and landmark papers.

Results: The findings show that the development of UMLS resources and tools have been led by interdisciplinary collaboration among medicine, biology, and computer science. Efforts encompassing multiple disciplines, such as medical informatics, biochemical sciences, and genetics, were the driving forces behind the domain's growth. The following topics were found to be the dominant research themes from the early phases to mid-phases: 1) development and extension of ontologies and 2) enhancing the integrity and accessibility of these resources. Knowledge discovery using machine learning and natural language processing and applications in broader contexts such as drug safety surveillance have recently been receiving increasing attention.

Discussion: Our analysis confirms that while reaching its scientific maturity, UMLS research aims to boundary-span to more variety in the biomedical context. We also made some recommendations for editorship and authorship in the domain.

Conclusion: The present study provides a systematic approach to map the intellectual growth of science, as well as a self-explanatory bibliometric profile of the published UMLS literature. It also suggests potential future directions. Using the findings of this study, the scientific community can better align the studies within the emerging agenda and current challenges.

Keywords: content analysis; science mapping; text mining; unified medical language system; visual analytics.

Figure 1.

Data distribution over time.

Figure 2.

Dual-map overlays: Core (upper) and Expanded (lower).

Figure 3.

Keyword co-occurrence networks: Core (upper; node = 173; edges = 945; density = 0.063) and Expanded (lower; node = 483; edges = 5337; density = 0.046).

Figure 4.

Document co-citation networks: Core (upper; node = 816; edges = 3311; density = 0.010; modularity = 0.773; clusters = 84) and Expanded (lower; node = 1833; edges = 9600; density = 0.006; modularity = 0.798; clusters = 156).