Named entity recognition in medical field based on BERT model
Bin Ma, Chen Lin · 2023
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.