Pre-Training to Identify Immunization-Related Entities from Systematic Reviews

Abstract

Entity recognition from semi or unstructured systematic reviews is one of the most essential processes for evidence-based decision-making systems. The task involves collecting information from diverse studies concerning PICO (Population, Intervention, Comparison, and Outcomes) elements with additional domain-related information using named entity recognition (NER) as it is the fundamental task for extracting the structured data. In this study, we create an adapted immunization-related dataset and evaluate its performance in the extraction of relevant entities from systematic reviews. We conducted experiments to investigate several models for entity recognition performance using language models pre-trained in the biomedical domain. Our results suggest that PubMedBERT and BertNER results are superior to the other models, and the immunization-related entities can be successfully recognized with a 76% F1 score and 92% accuracy.