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Creating a Sustainable Large-Scale Content-Based Biomedical Article Classifier Using BERT
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Author(s): Aakash Jayakumar (SRM Institute of Science and Technology, India), Kavya Saketharaman (SRM Institute of Science and Technology, India), J. Arthy (SRM Institute of Science and Technology, India)and S. Jayabharathi (SRM Institute of Science and Technology, India)
Copyright: 2024
Pages: 14
Source title:
Cross-Industry AI Applications
Source Author(s)/Editor(s): P. Paramasivan (Dhaanish Ahmed College of Engineering, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Karthikeyan Chinnusamy (Veritas, USA), R. Regin (SRM Instıtute of Science and Technology, India)and Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand)
DOI: 10.4018/979-8-3693-5951-8.ch018
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Abstract
Given the scarcity of labeled corpora and the high costs of human annotation by qualified experts, clinical decision-making algorithms in biomedical text classification require a significant number of costly training texts. To reduce labeling expenses, it is common practice to use the active learning (AL) approach to reduce the volume of labeled documents required to produce the required performance. There are two methods for categorizing articles: article-level classification and journal-level classification. In this chapter, the authors present a hybrid strategy for training classifiers with article metadata such as title, abstract, and keywords annotated with the journal-level classification FoR (fields of research) using natural language processing (NLP) embedding techniques. These classifiers are then applied at the article level to analyze biomedical publications using PubMed metadata. The authors trained BERT classifiers with FoR codes and applied them to classify publications based on their available metadata.
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