Named entity recognition in medical field based on BERT model

Bin Ma; Lin Chen

doi:10.1117/12.3011763

7 December 2023 Named entity recognition in medical field based on BERT model

Bin Ma, Lin Chen

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294130 (2023) https://doi.org/10.1117/12.3011763
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Chinese medical named entity recognition is a crucial task in information extraction within the medical field. Its primary objective is to extract entities of medical significance, such as diseases, clinical manifestations, and drugs, from medical texts. Due to the specialized nature of the medical domain, the grammatical structure of medical texts is often more complex than that of other texts, frequently containing a plethora of abbreviations and terms. To enhance the effectiveness of Chinese medical entity recognition, this paper introduced a pre-trained BERT-BiLSTM-CRF model, the CHIP2020 medical text dataset was chosen for the experiment, including 9 types of entities, such as diseases, clinical manifestations, drugs, medical equipment, medical procedures, body parts, medical examination items, microorganisms, and departments. BERT effectively utilizes context information to comprehend the semantics and relevance of words in sentences, effectively resolving the challenge of word polysemy and exhibiting strong language representation capabilities. Additionally, BiLSTM is employed to extract features, efficiently capturing the sequence information and context between words. Finally, CRF is utilized to convert the probability sequence output from BiLSTM into the optimal annotation sequence. Experimental results demonstrate that, compared with three other models, the proposed method yields improved accuracy in named entity recognition on the CHIP2020 dataset, with an impressive F1 value of 64.93%. This approach effectively extracts medical entities.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bin Ma and Lin Chen "Named entity recognition in medical field based on BERT model", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294130 (7 December 2023); https://doi.org/10.1117/12.3011763

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KEYWORDS

Data modeling

Performance modeling

Machine learning

Semantics

Deep learning

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