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Investigation of Biomedical Named Entity Recognition Methods

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4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2022)

Part of the book series: Engineering Cyber-Physical Systems and Critical Infrastructures ((ECPSCI,volume 7))

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Abstract

Biomedical named-entity recognition is the process of identifying entity names such as disease, symptom, drug, protein, and chemical in biomedical texts. It plays an important role in natural language processing, such as relationship extraction, question-answer systems, keyword extraction, machine translation, and text summarization. Biomedical domain information extraction can be used for early diagnosis of diseases, detection of missing relationships between biomedical entities such as diseases and chemicals, and determination of drug interactions and side effects. Since biomedical texts contain domain-specific words, complicated phrases, and abbreviations, named entity recognition in this domain is still a challenging task. In this study, we first investigated methods for named entity recognition in the biomedical domain. These methods are classified into four categories: dictionary-based, rule-based, machine learning, and deep learning methods. Recent advances such as deep learning and transformer-based biomedical language models have helped to achieve successful results in the named entity recognition task. Second, we conduct an experimental study on an annotated dataset called MedMention which is available to researchers. Finally, we present our experimental results and discuss the challenges and opportunities of the existing methods. The experimental study shows that the most successful method for extracting diseases and symptoms from biomedical texts is BioBERT, with an F1 score of 0.72.

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Author information

Authors and Affiliations

  1. Manisa Celal Bayar University, Software Engineering, Manisa, Turkey

    Azer Çelikten

  2. İzmir Katip Çelebi University, Computer Engineering, İzmir, Turkey

    Aytuğ Onan

  3. Ege University, Computer Engineering, İzmir, Turkey

    Hasan Bulut

Authors
  1. Azer Çelikten
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  2. Aytuğ Onan
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  3. Hasan Bulut
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Corresponding author

Correspondence to Azer Çelikten .

Editor information

Editors and Affiliations

  1. Department of ECE, Karunya University, Karunya Nagar, Tamil Nadu, India

    D. Jude Hemanth

  2. Department of Computer Engineering, Faculty of Engineering, Süleyman Demirel University, Isparta, Türkiye

    Tuncay Yigit

  3. Department of Computer Engineering, Suleyman Demirel University, Isparta, Türkiye

    Utku Kose

  4. Electric-Electronic Department, Duzce University, Düzce, Türkiye

    Ugur Guvenc

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Çelikten, A., Onan, A., Bulut, H. (2023). Investigation of Biomedical Named Entity Recognition Methods. In: Hemanth, D.J., Yigit, T., Kose, U., Guvenc, U. (eds) 4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering. ICAIAME 2022. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-031-31956-3_18

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