Biomaterials text mining: A hands-on comparative study of methods on polydioxanone biocompatibility

Scientific information extraction is fundamental for research and innovation, but is currently mostly a manual, time-consuming process. Text Mining tools (TMTs) enable automated, accurate and quick information extraction from text, but there is little precedent of their use in the biomaterials field...

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Bibliographic Details
Published in:New biotechnology 2023-11, Vol.77, p.161-175
Main Authors: Fuenteslópez, Carla V., McKitrick, Austin, Corvi, Javier, Ginebra, Maria-Pau, Hakimi, Osnat
Format: Article
Language:English
Subjects:
Biocompatibility
Biomaterials
Information extraction
Polydioxanone
Text mining
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Summary:Scientific information extraction is fundamental for research and innovation, but is currently mostly a manual, time-consuming process. Text Mining tools (TMTs) enable automated, accurate and quick information extraction from text, but there is little precedent of their use in the biomaterials field. Here, we compare the ability of various TMTs to extract useful information from biomaterials abstracts. Focusing on the biocompatibility of polydioxanone, a biodegradable polymer for which there are relatively few scientific publications, we tested several tools ranging from machine learning approaches and statistical text analysis to MeSH indexing and domain-specific semantic tools for Named Entity Recognition. We also evaluated their output alongside a manual review of systematic reviews and meta-analyses. The findings show that TMTs can be highly efficient and powerful for mapping biomaterials texts and rapidly yield up-to-date information. Here, TMTs enable one to identify dominating themes, see the evolution of specific terms and topics, and learn about key medical applications in biomaterials literature over the years. The analysis also shows that ambiguity around biomaterials nomenclature is a significant challenge in mining biomedical literature that is yet to be tackled. This research showcases the potential value of using Natural Language Processing and domain-specific tools to extract and organize biomaterials data. [Display omitted] •This is the first hands-on application of text mining tools (TMT) in Biomaterials.•We apply multiple TMTs to extract information from the Biomaterials literature.•TMTs produce an informative research map of polydioxanone with main topics & trends.•TMTs also highlight research gaps, missing assets & unresolved obstacles.•We showcase NER’s potential to extract deep data & drive discoveries in Biomaterials.
ISSN:1871-6784
1876-4347
DOI:10.1016/j.nbt.2023.09.001