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Named Entity Recognition in Electronic Medical Records Based on Transfer Learning
ID：54 View Protection：ATTENDEE Updated Time：2025-10-11 22:35:10 Hits：228 Poster Presentation

Start Time：2025-11-09 09:04（Asia/Shanghai）

Duration：1min

Session：P Poster presentation » P66.AI-driven technology

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Abstract
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.
Keywords
BERT-BiLSTM-CRF,Chinese electronic medical records,named entity recognition,transfer learning
Speaker
shuyu qian
学生 西南民族大学

Submission Author
shuyu qian 西南民族大学
Duyu Liu 西南民族大学
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Important Date

  • Conference Date

    Nov 07

    2025

    to

    Nov 09

    2025

  • Oct 30 2025

    Draft paper submission deadline

  • Nov 10 2025

    Registration deadline

Sponsored By
IEEE西南交通大学IAS学生分会
Organized By
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
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