Abstract
The MEDLINE database of medical literature is routinely used by researchers and doctors to find articles pertaining to their area of interest. Insight into historical changes in research areas may be gained by chronological analysis of the 18 million records currently in the database, however such analysis is generally complex and time consuming. The authors' MLTrends web application graphs term usage in MEDLINE over time, allowing the determination of emergence dates for biomedical terms and historical variations in term usage intensity. MLTrends may be used at: http://www.ogic.ca/mltrends.
Highlights
MEDLINE is a medical and life sciences bibliographic database containing more than 18 million records ranging back to 1950
Besides its primary use as a tool for reference search, MEDLINE mirrors the progress of biomedical research and analysis of the terms used in large numbers of articles can provide insights into the evolution of ideas and terminology in the field [2, 3], which can be of interest to researchers and to educators, journalists, and policy makers
The utility of visualizing temporal term usage analysis in MEDLINE with MLTrends is illustrated here by exploring the timeline of events surrounding the discovery that a variant of the Creutzfeldt‐Jakob disease, a human neurodegenerative disease, is caused by consumption of brain tissue from cows affected by Bovine Spongiform Encephalopathy (BSE), and the eventual explanation of transmission by prion infection
Summary
MEDLINE is a medical and life sciences bibliographic database containing more than 18 million records ranging back to 1950. It is available over the web via the PubMed web interface and is an important resource used by researchers and doctors worldwide [1]. To simplify the visualization of historical variations in term usage intensity for multiple terms simultaneously we have implemented MLTrends (http://www.ogic.ca/mltrends) (Figure 1) This tool allows users to graph term usage per year in MEDLINE entries. A further normalization option offered is by total number of word instances in the records of the year. Normalizing by the number of word instances in all records of the year somehow avoids an artifactual jump
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