报告详情
Named Entity Recognition in Electronic Medical Records Based on Transfer Learning
编号：54
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更新：2025-10-11 22:35:10 浏览：2次
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摘要
To address the challenges of scarce labeled data and cross-disease knowledge transfer in named entity recognition (NER) tasks for Chinese electronic medical records, this paper proposes a transfer learning method based on the BERT-BiLSTM-CRF model to explore cross-disease knowledge transfer strategies. By comparing experimental results in non-transfer and transfer learning scenarios, we systematically explore the impact of the number of target domain samples and the ratio of source and target domain data on model performance. Baseline model experiments show that differences in data distribution have a significant impact on entity recognition performance; after introducing transfer learning, the model's recognition performance in the target domain (especially in small sample scenarios) is significantly improved. This research provides an effective technical solution for low-resource medical text processing.
关键词
BERT-BiLSTM-CRF,Chinese electronic medical records,named entity recognition,transfer learning
报告人
shuyu qian
学生 西南民族大学稿件作者
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重要日期
-
会议日期
11月07日
2025
至11月09日
2025
-
10月12日 2025
初稿截稿日期
-
10月20日 2025
注册截止日期
主办单位
IEEE西南交通大学IAS学生分会
承办单位
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
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