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Recognizing software names in biomedical literature using machine learning.

Qiang Wei, Yaoyun Zhang, Muhammad Amith¹

¹The University of Texas Health Science Center at Houston, USA.

Health Informatics Journal

|October 1, 2019

Summary

This summary is machine-generated.

This study developed machine learning models to automatically identify biomedical software from research papers, creating a large software catalog. These natural language processing methods offer a scalable solution for indexing scientific software.

Keywords:

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

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Area of Science:

Biomedical Informatics
Computational Biology
Natural Language Processing

Background:

Biomedical research relies heavily on software tools, but existing repositories are difficult to scale due to manual curation.
There is a need for automated methods to index and catalog software used in the biomedical domain.

Purpose of the Study:

To develop and evaluate machine learning-based named entity recognition systems for automatically identifying biomedical software from literature.
To create a comprehensive biomedical software catalog using automated methods.

Main Methods:

Manually annotated a corpus of 1,120 MEDLINE abstracts and titles for software names.
Developed named entity recognition systems using domain knowledge features and unsupervised word embeddings.
Evaluated system performance using F-measure with inexact matching criteria.

Main Results:

The best system achieved an F-measure of 91.79% for software recognition in titles and 86.35% in titles and abstracts.
A biomedical software catalog with 19,557 entries was successfully created using the developed system.

Conclusions:

Natural language processing methods are feasible for automatically building a high-quality software index from biomedical literature.
Automated software cataloging can overcome the scalability limitations of manual curation, supporting biomedical research.