高准确度参数高效微调,用于联合识别和将诊断与非标准初级保健笔记中的ICD-10联系起来
Cristian Estupiñán-Ojeda1, Raúl J Sandomingo-Freire2, Lluís Padró2
1SIANI, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35017, Spain.
JAMIA open
|October 20, 2025
概括
参数效率微调 (PEFT) 方法,特别是QLoRA,为联合实体认可和ICD-10在临床笔记中的链接提供了一种资源意识的方法. 这些技术实现了接近完全微调的性能,显著减少了可训练参数和内存使用.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 机器学习 机器学习
背景情况:
- 在双语临床笔记中,联合实体认可和ICD-10链接是复杂的.
- 初级保健笔记中的非标准语言带来了额外的挑战.
- 对于临床文本分类,全微调 (FFT) 在计算上是昂贵的.
研究的目的:
- 评估参数效率微调 (PEFT) 技术作为对FFT的资源有意识的替代方案.
- 评估各种PEFT方法在多标签临床文本分类任务中的性能.
- 确定最有效的PEFT策略,用于在低资源环境中承认联合实体和ICD-10链接.
主要方法:
- 将PEFT技术 (LoRA,DoRA,LoHA,LoKR,QLoRA) 与FT进行比较.
- 使用多语言变压器 (BERT,RoBERTa,DistilBERT,mDeBERTa) 在21,812个加泰罗尼亚语和西班牙语临床笔记的语料库上.
- 在综合双语数据上训练模型以评估概括性.
主要成果:
- 伯特-QLoRA的业绩 (微型F1:62.2) 仅低于FFT (63.0) 的0.8个点.
- 与FFT相比,BERT-QLoRA减少了67.5%的可训练参数和33.7%的内存.
- 结合双语数据的培训提高了跨语言的概括性.
结论:
- PEFT,特别是QLoRA,为联合实体识别和ICD-10链接提供了准确且有效的内存解决方案.
- PEFT可以在商品GPU上部署,性能接近FFT.
- 在评估的PEFT技术中,QLoRA表现出最强的准确性-效率平衡.
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