Identifying Medical Named Entities with Word Information
Ben Yanyan1,Pang Xueqin2()
1School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China 2Archives of Wuhan University of Science and Technology, Wuhan 430081, China
[Objective] This paper utilizes the word information to identify and infer the key clinical features in online consultation records and address the difficulty in recognizing the boundaries of named entities. [Methods] First, we constructed a new model based on MacBERT and conditional random fields. Then, we embedded the word position and part of speech as the dialogue text information by the speaker role embedding. Finally, we used the weighted multi-class cross-entropy to solve the problem of entity category imbalance. [Results] We conducted an empirical study with online consultation records from Chunyu Doctor. The F1 value of the proposed model in the named entity recognition task was 74.35%, which was nearly 2% higher than directly using the MacBERT model. [Limitations] We did not design a specific model for Chinese word segmentation. [Conclusions] Our new model with more dimensional features can effectively improve its ability to recognize key features of clinical findings.
本妍妍, 庞雪芹. 融入词性的医疗命名实体识别研究*[J]. 数据分析与知识发现, 2023, 7(5): 123-132.
Ben Yanyan, Pang Xueqin. Identifying Medical Named Entities with Word Information. Data Analysis and Knowledge Discovery, 2023, 7(5): 123-132.
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