Leveraging network analysis to evaluate biomedical named entity recognition tools

• Published in: Scientific reports Vol. 11; no. 1; p. 13537
• Main Authors: García del Valle, Eduardo P., Lagunes García, Gerardo, Prieto Santamaría, Lucía, Zanin, Massimiliano, Menasalvas Ruiz, Ernestina, Rodríguez-González, Alejandro
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.06.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1386; 631/114/2406; 631/114/2408; 631/1647/48; 639/705/1046; 692/308; 692/699; Accuracy; Annotations; Automation; Datasets; Disease; Humanities and Social Sciences; Hypotheses; Medical records; Methods; multidisciplinary; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-93018-w

The ever-growing availability of biomedical text sources has resulted in a boost in clinical studies based on their exploitation. Biomedical named-entity recognition (bio-NER) techniques have evolved remarkably in recent years and their application in research is increasingly successful. Still, the disparity of tools and the limited available validation resources are barriers preventing a wider diffusion, especially within clinical practice. We here propose the use of omics data and network analysis as an alternative for the assessment of bio-NER tools. Specifically, our method introduces quality criteria based on edge overlap and community detection. The application of these criteria to four bio-NER solutions yielded comparable results to strategies based on annotated corpora, without suffering from their limitations. Our approach can constitute a guide both for the selection of the best bio-NER tool given a specific task, and for the creation and validation of novel approaches.