---
id: 20260304-T0-13
title: "BERT模型降噪技术提升临床实体识别"
title_en: "BERT Noise Reduction Boosts Clinical Entity Recognition"
url: https://ai.daily.yangsir.net/daily/20260304-T0-13
issue_date: 2026-03-04
publish_date: 2026-03-03T05:00:00.000Z
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2603.00022
---

# BERT模型降噪技术提升临床实体识别

研究团队改进BERT模型的命名实体识别（NER）方法，优化临床文本中的实体提取精度。新方法通过引入动态降噪层，将实体识别的F1分数从82.7%提升至89.3%，尤其在罕见疾病术语识别上错误率降低45%。该模型在10万份真实病历测试中，实体提取速度比传统方法快2倍，已开源代码供医疗机构使用。

## English Version

**BERT Noise Reduction Boosts Clinical Entity Recognition**

A research team improved BERT's Named Entity Recognition (NER) method to optimize entity extraction accuracy in clinical text. The new method introduces a dynamic denoising layer, increasing the F1 score from 82.7% to 89.3% and reducing error rates by 45% for rare disease terminology recognition. Tested on 100,000 real medical records, the model extracts entities twice as fast as traditional methods and its code is open-sourced for medical institutions.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2603.00022)

**详情页**：https://ai.daily.yangsir.net/daily/20260304-T0-13

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