TY  - JOUR
T1  - Pre-Training to Identify Immunization-Related Entities from Systematic Reviews
AU  - İlgen, Bahar
AU  - Pilic, Antonia
AU  - Harder, Thomas
AU  - Hattab, Georges
AB  - 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.
KW  - Named entity recognition
KW  - Systematic reviews
KW  - Immunization
KW  - Vaccination
KW  - BERT
KW  - PICO
KW  - Evidence based medicine
KW  - 610 Medizin und Gesundheit
PY  - 2024
LA  - eng
PB  - Robert Koch-Institut
JO  - Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval
SP  - 234
EP  - 239
DO  - 10.1145/3639233.3639355
ER  -