Recognizing software names in biomedical literature using machine learning

Qiang Wei, Yaoyun Zhang, Muhammad Amith, Rebecca Lin, Jenay Lapeyrolerie, Cui Tao, Hua Xu

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Software tools now are essential to research and applications in the biomedical domain. However, existing software repositories are mainly built using manual curation, which is time-consuming and unscalable. This study took the initiative to manually annotate software names in 1,120 MEDLINE abstracts and titles and used this corpus to develop and evaluate machine learning–based named entity recognition systems for biomedical software. Specifically, two strategies were proposed for feature engineering: (1) domain knowledge features and (2) unsupervised word representation features of clustered and binarized word embeddings. Our best system achieved an F-measure of 91.79% for recognizing software from titles and an F-measure of 86.35% for recognizing software from both titles and abstracts using inexact matching criteria. We then created a biomedical software catalog with 19,557 entries using the developed system. This study demonstrates the feasibility of using natural language processing methods to automatically build a high-quality software index from biomedical literature.

Original languageEnglish (US)
Pages (from-to)21-33
Number of pages13
JournalHealth Informatics Journal
Issue number1
StatePublished - Mar 1 2020
Externally publishedYes


  • biomedical literature
  • biomedical software
  • biomedical software index
  • named entity recognition
  • natural language processing

ASJC Scopus subject areas

  • Health Informatics


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