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.

Something New and Different: The Unified Medical Language System

“…Because it is both an evolving set of artifacts and a set of ideas, it is hard to find, over the ensuing nearly 40 years, a large biomedical information project that has not been influenced by it. Today, as a testament to Lindberg's foresight, the UMLS is infrastructure -heavily used, but not always cited [12,[33][34]. As described here, it had no precedent, and, thus, initially, application developers, and their end-users, had difficulty applying it.…”

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

“…For example, Hernandez-Alvarez et al [23] used high-frequency keywords in abstracts, manually identified important terms to generate knowledge domains, and divided the literature into multiple knowledge domains by calculating the similarity between them to classify the literature. Similarly, Kim et al [25] used keywords and abstracts as raw data to create a document terminology matrix representing the frequency of terms in the accounting literature. Makabate et al [3] used a grounded theory approach to code the terms collected in the abstracts and classify research methods.…”

Mapping Global Knowledge Domain, Research in Information Retrieval in Medical Sciences: A Scientometric and Evaluative Study

“…As mentioned, in the eld of information retrieval in medical sciences, until this research was done, no scientometric study or KMD has been conducted to analyze research in this eld. In the most relevant research, Kim et al have used KMD methods to examine historical footprints, emerging technologies, and challenges in using UMLS resources and tools to present potential future directions (Kim et al 2020). But KMD has been used in many studies in speci c elds of medical sciences.…”