Identifying Medical Named Entities with Word Information

被引：0
|
作者：
Ben Y. [1 ]
Pang X. [2 ]
机构：
[1] School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan
[2] Archives of Wuhan University of Science and Technology, Wuhan
来源：
Data Analysis and Knowledge Discovery | 2023年 / 7卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Chinese Named Entity Recognition; MacBERT; Online Medical Consultation; Weighted Cross Entropy; Word Information Embedding;
D O I：
10.11925/infotech.2096-3467.2022.0547
中图分类号：
学科分类号：
摘要：
[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. © 2023 Data Analysis and Knowledge Discovery. All rights reserved.
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收藏
页码：123 / 132
页数：9
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