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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control J-SAKTI (Jurnal Sains Komputer dan Informatika) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal IMPACT: Implementation and Action J-SAKTI (Jurnal Sains Komputer dan Informatika) Insearch: Information System Research Journal
Kusuma, Selvia Ferdiana
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

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Named Entity Recognition in Medical Domain: A systematic Literature Review Kusuma, Selvia Ferdiana; Wibowo, Prasetyo; Abdillah, Abid Famasya; Basuki, Setio
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3111

Abstract

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.
Co-Authors Abdillah, Abid Famasya Agung Teguh Wibowo Almais Agustono Heriadi, Agustono Aminulloh, Septian Wijaya Ashafidz Fauzan Dianta, Ashafidz Fauzan Basuki, Setio Chastine Fatichah Daniel Oranova Siahaan Fadelis Sukya, Fadelis Halimahtus Mukminna, Halimahtus Mohammad Farid Naufal, Mohammad Farid Perdananto, Bayu Rizki Putra, Putu Aditya Riva Ramadhan, Rizal Furqan Ratna Widyastuti, Ratna Ratri Enggar Pawening Rinanza Zulmy Alhamri, Rinanza Zulmy Rohman Dijaya Vernolyo, Panji Yumadana Wibowo, Prasetyo