A Biopython-based method for comprehensively searching for eponyms in Pubmed


Eponyms are common in medicine; however, their usage has varied between specialties and over time. A search of specific eponyms will reveal the frequency of usage within a medical specialty. While usage of eponyms can be studied by searching PubMed, manual searching can be time-consuming. As an alternative, we modified an existing Biopython method for searching PubMed. In this method, a list of disease eponyms is first manually collected in an Excel file. A Python script then creates permutations of the eponyms that might exist in the cited literature. These permutations include possessives (e.g., ’s) as well as various forms of combining multiple surnames. PubMed is then automatically searched for this permutated library of eponyms, and duplicate citations are removed. The final output file may then be sorted and enumerated by all the data fields which exist in PubMed. This method will enable rapid searching and characterization of eponyms for any specialty of medicine. This method is agnostic to the type of terms searched and can be generally applied to the medical literature including non-eponymous terms such as gene names and chemical compounds.*Custom Python scripts using Biopython’s Bio.Entrez module automate the search for medical eponyms.*This method can be more broadly used to search for any set of terms existing in PubMed.

Toby C. Cornish
Toby C. Cornish
Professor of Pathology and Data Science Institute

Clinical informaticist, gastrointestinal pathologist, and researcher.