充分利用大型语言模型用于临床缩写清晰化
Manda Hosseini1, Mandana Hosseini2, Reza Javidan2
1Department of Computer Engineering, Zand Institute of Higher Education, Shiraz, Iran. manda.hosseinii@gmail.com.
Journal of medical systems
|February 27, 2024
概括
这项研究使用大型语言模型 (LLM) 和生成模型 (BIOGPT) 来增强数据,增强了临床缩写的明确性. 该方法通过生成与上下文相关的示例,提高了准确性,特别是对于罕见的缩写.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 临床数据分析 临床数据分析
背景情况:
- 临床缩写的明确性对于医疗信息检索至关重要.
- 现有方法面临的挑战是有限的数据和模两可的缩写.
- 准确的缩写解释对于临床文本分析至关重要.
研究的目的:
- 通过使用大型语言模型 (LLM) 和数据增强来改善临床缩写的明确性.
- 解决临床文本中有限的实例和模两可的解释所带来的挑战.
- 利用BIOGPT生成上下文相关数据,以提高清晰度的准确性.
主要方法:
- 在LLMs中利用BlueBERT和变压器进行上下文理解.
- 采用生物医学文献预训练的生成模型 (BIOGPT) 来进行数据增强.
- 为缩写扩展生成多样化,与背景相关的临床文本实例.
- 对CASI数据集的方法进行了评估,分为培训,验证和测试集.
主要成果:
- 使用生成模型增强数据显著提高了清晰度的性能.
- 增强的性能特别值得注意的是,在有限的实例中,缩写感觉具有有限的实例.
- 该方法有效地解决了数据集失衡和类似临床概念带来的挑战.
- 拟议的模型在测试组中取得了良好的准确性,超过了以前的方法.
结论:
- 在临床缩写清晰化方面,LLM和生成技术是有效的.
- 使用BIOGPT的数据增强成功地解决了数据稀缺性和模糊性问题.
- 该方法为提高临床文本分析的准确性提供了一个强大的解决方案.
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