ArticleJournal of biomedical informatics2026
A data-driven method for research trend analysis in a scientific discipline: Application to the journal of biomedical informatics.
Article in Journal of biomedical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
8 authors.
Funding
Abstract
objectiveAccurately characterizing research trends is critical for identifying cutting-edge scientific breakthroughs in their infancy and informing strategic priorities. This research contributes a pipeline that utilizes generative AI technologies to develop research topic taxonomies from publication keywords and analyze keyword evolution within topics, methodological and domain trends, and topic co-occurrences. We demonstrated the pipeline by conducting a retrospective analysis of biomedical informatics research trends in the Journal of Biomedical Informatics (JBI).
methodsWe identified the JBI publications with keywords available on PubMed, spanning 2011-2025. We downloaded all the keywords and categorized them into methodological innovations and health domains, identified topics, assigned topic names, and constructed their hierarchies, all using large-language models (LLMs). We introduced an automated method for evaluating topics, leveraging MeSH terminology as the underlying knowledge base.
resultsUsing 6,930 unique keywords from 2,427 publications, we derived 1,028 distinct topics related to methodological innovations, with each topic associated with medians of four keywords (Q1: 2, Q3: 13) and six publications (Q1: 2, Q3: 19). We identified 904 topics related to health domains, with each topic associated with three keywords (Q1: 1, Q3: 11) and four publications (Q1: 1, Q3: 15). Based on the topics, we analyzed the prominent research areas, trends in publication volume, evolution of keyword distributions within each topic, and patterns of co-occurring topics. Among the 2,379 eligible publications, 2,009 (84.4%) exhibited overlap between the keyword-derived MeSH terms and the MeSH terms assigned to the publication by the National Library of Medicine.
conclusionThis study presents a method that leverages modern generative AI technologies for retrospective analysis of a scientific field to identify emerging topics and to detect shifts in scholarly focus. Illustrated by data for JBI and correlated with historical background events and policy changes, our findings demonstrate the effectiveness and utility of the methods while providing a powerful lens to understand the evolution of biomedical informatics research priorities in JBI.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.