Human Indexing of Citations in MEDLINE Essay

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MEDLINE is a reputable database of scientific knowledge in the sphere of medicine with more than a hundred years of history. One of its traditions is the use of human citation indexing that was introduced as far back as 1879 (Mork, Aronson, & Demner-Fushman, 2017). Augmented with recent technological advancements that help ease the manual work of scientific personnel, this practice continues to exist. Despite the doubts of its relevance with the advent of full-text search, its use provides a unique searching experience that is still quite popular.

Citation indexes are an essential practice in research as it allows for the searching of information, as well as the works of other people who have studied in the same area (U.S. National Library of Medicine, n.d.a). With the emergence of online databases, citation indexing has become one of the criteria by which its reputability and popularity are measured. MEDLINE contains 24 million article references which can be found through indexing (U.S. National Library of Medicine, n.d.b). Since its introduction, it has undergone some enhancements, such as the Medical Text Indexer (MTI) system that utilizes natural language processing (NLP) technology. This system enables the annual processing of more than 760,000 citations, from 5600 biomedical journals, in 36 languages (Mork et al., 2017). The scale itself could be a perfect measure of the effectiveness and popularity of the system. In addition, according to the study by Mork et al. (2017), articles recommended through the MTI indexing system demonstrated a steady 40% usage growth in the period 2002 – 2014.

Full-text search is a relatively recent invention. It first became available in the 1990s and allows for searching a body of text or a full-sentence within a corpus of texts. The problem with this approach is that within a densely-filled database, with a multitude of unstructured texts, the results of the search are rather unsatisfying (Natarajan, Bruchez, Coles, Shaw, & Cebollero, 2015). In addition, the technology has a rather low precision rate, attributable to the language and the problems of programming a mechanism that would be able to cope with its ambiguity. Full-text search is rather convenient due to the ability to find the exact words if a user knows them. The technique also incorporates indexing and adds keywords and stop words that increase the precision of a query. Full-text search is always advancing and uses a variety of search algorithms, including the field-restricted, fuzzy, phrase, and Boolean.

However, in scientific culture, when an author uses other scientists’ ideas, it is required that they should paraphrase them and cite the source. In these circumstances, a full-text search is not particularly useful. Mork et al. (2017) see the future in combining the strong sides of those two approaches and advancing MTI searches of MEDLINE through the extended use of full-text queries.

All things considered, human indexing with the use of MTI does not make MEDLINE’s search system obsolete with the advent of full-text queries. The latter should be seen rather as a technological upgrade that enhances the effectiveness of MEDLINE as a database.

References

Mork, J., Aronson, A., & Demner-Fushman, D. (2017). 12 years on – Is the NLM medical text indexer still useful and relevant? Journal of Biomedical Semantics, 8(1), 8.

Natarajan, J., Bruchez, R., Coles, M., Shaw, S., Cebollero, M. (2015). Pro T-SQL programmer’s guide. Berkeley, CA: Apress.

U.S. National Library of Medicine. (n.d.a). Web.

U.S. National Library of Medicine. (n.d.b). Web.

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