Named Entity Recognition in Medical Domain: A systematic Literature Review

Biomedical Named Entity Recognition (BioNER) is essential to bioinformatics because it identifies and classifies biological entities in biomedical texts. With the increasing number of biomedical literature and the rapid progress of the BioNER approach, it is essential to conduct a systematic literature review (SLR) on BioNER. This SLR consolidates existing information and provides directions for future studies in the BioNER field. This review systematically explores scientific journals and conferences published from 2019 to 2024. This research uses PubMed and Scholar as reference search databases because of their affiliation with other well-known publishers such as IEEE, Elsevier, and Springer. The results show a transition from conventional machine learning to deep learning. Neural networks and transformers show better performance in deep learning methods. The datasets often used in BioNER development are BC2GM, BC5CDR, and NCBI-Disease. Precision, Recall, and F1-Score are used in most papers to evaluate model performance. The performance of these models mostly depends on the availability of big annotated datasets and significant computational tools. Therefore, it is vital for future research to address the issues of annotated data and resource availability to build accurate models. Researchers should investigate the creation of ideal designs that lower computing complexity without compromising performance. Overall, this SLR offers a thorough overview of the latest research on BioNER. It provides significant insights for academics and practitioners in bioinformatics and medical research, helping them understand the innovative aspects of BioNER research.