AI-Based Knowledge Extraction from the Bioprinting Literature for Identifying Technology Trends.

ABSTRACT

Bioprinting is a rapidly evolving field, as represented by the exponential growth of articles and reviews published each year on the topic. As the number of publications increases, there is a need for an automatic tool that can help researchers do more comprehensive literature analysis, standardize the nomenclature, and so accelerate the development of novel manufacturing techniques and materials for the field. In this context, we propose an automatic keyword annotation model, based on Natural Language Processing (NLP) techniques, that can be used to find insights in the bioprinting scientific literature. The approach is based on two main data sources, the abstracts and related author keywords, which are used to train a composite model based on (i) an embeddings part (using the FastText algorithm), which generates word vectors for an input keyword, and (ii) a classifier part (using the Support Vector Machine algorithm), to label the keyword based on its word vector into a manufacturing technique, employed material, or application of the bioprinted product. The composite model was trained and optimized based on a two-stage optimization procedure to yield the best classification performance. The annotated author keywords were then reprojected on the abstract collection to both generate a lexicon of the bioprinting field and extract relevant information, like technology trends and the relationship between manufacturing-material-application. The proposed approach can serve as a basis for more complex NLP-related analysis toward the automated analysis of the bioprinting literature.