Automated indexing using NLM's Medical Text Indexer (MTI) compared to human indexing in Medline: a pilot study

• Published in: Journal of the Medical Library Association Vol. 111; no. 3; pp. 684 - 695
• Main Authors: Chen, Yin Yin, Bullard, Julia, Giustini, Dean
• Format: Journal Article
• Language: English
• Published: United States Medical Library Association 10.07.2023; University Library System, University of Pittsburgh
• Subjects: Abstracting and Indexing - methods; Abstracting and Indexing - standards; Automated indexing; human indexers; Indexing; information retrieval; Libraries; Medical Subject Headings; Medical Text Indexer (MTI); MEDLINE; National Library of Medicine (U.S.); Original Investigation; Pilot Projects; PubMed; Rankings; Technology application; United States; United States; Automated indexing; Medline; PubMed; Medical Text Indexer (MTI); information retrieval; human indexers
• ISSN: 1536-5050; 1558-9439; 1558-9439
• DOI: 10.5195/jmla.2023.1588

Objective: In 2002, the National Library of Medicine (NLM) introduced semi-automated indexing of Medline using the Medical Text Indexer (MTI). In 2021, NLM announced that it would fully automate its indexing in Medline with an improved MTI by mid-2022. This pilot study examines indexing using a sample of records in Medline from 2000, and how an early, public version of MTI's outputs compares to records created by human indexers. Methods: This pilot study examines twenty Medline records from 2000, a year before the MTI was introduced as a MeSH term recommender. We identified twenty higher- and lower-impact biomedical journals based on Journal Impact Factor (JIF) and examined the indexing of papers by feeding their PubMed records into the Interactive MTI tool. Results: In the sample, we found key differences between automated and human-indexed Medline records: MTI assigned more terms and used them more accurately for citations in the higher JIF group, and MTI tended to rank the Male check tag more highly than the Female check tag and to omit Aged check tags. Sometimes MTI chose more specific terms than human indexers but was inconsistent in applying specificity principles. Conclusion: NLM’s transition to fully automated indexing of the biomedical literature could introduce or perpetuate inconsistencies and biases in Medline. Librarians and searchers should assess changes to index terms, and their impact on PubMed’s mapping features for a range of topics. Future research should evaluate automated indexing as it pertains to finding clinical information effectively, and in performing systematic searches.