用BERT和大型语言模型组合提高临床注释部分分类模型的可转移性
Weipeng Zhou1, Dmitriy Dligach2, Majid Afshar3
1Department of Biomedical, Informatics and Medical Education, School of Medicine, University of Washington.
概括
大型语言模型 (LLM) 与传统模型相比,在电子健康记录部分的分类方面表现优越. 将LLM与监督方法相结合,通过组合技术进一步提高了准确性.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 电子健康记录 (EHR) 包含有价值的信息,分为不同的部分.
- 对于医疗保健中的各种下游应用而言,精确分类EHR部分至关重要.
- 当前的方法经常在不同数据集的概括性方面扎.
研究的目的:
- 为了提高EHR部分分类模型的可转移性.
- 为了利用大型语言模型 (LLM) 的世界知识,以及特定数据集的知识.
- 在这个领域调查零射击LLM的性能.
主要方法:
- 使用大型语言模型 (LLM) 进行零射击部分分类.
- 将LLM业绩与监督的BERT模型进行比较.
- 采用简单的组合技术,结合模型的优势.
主要成果:
- 零射击的LLM在域外数据上表现优于监督的BERT模型.
- 结合LLMs和监督模型时观察到协同效应.
- 组合方法带来了显著的额外性能增长.
结论:
- LLM提供了一种强大的,可转移的方法来对EHR部分分类.
- 结合LLMs和监督学习的混合方法非常有效.
- 未来的工作应该探索先进的组合策略,以改进EHR分析.
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