在临床叙述中用大型语言模型对缩写的明确化
Amila Kugic1, Stefan Schulz1, Markus Kreuzthaler1
1Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria.
概括
大型语言模型 (LLM) 显示出临床缩写解读的前景. GPT-4在英语中表现出色,但需要进一步开发德语和葡萄牙语,而Llama模型还不适合.
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
- 人工智能的人工智能
背景情况:
- 临床叙述包含许多缩写词,对自动化处理和理解构成挑战.
- 有效地消除这些缩写的模糊性对于改善临床文本可读性和数据分析至关重要.
研究的目的:
- 评估各种大型语言模型 (LLM) 在临床叙述中的零射击缩写解读的性能.
- 为了比较不同语言 (英语,德语,葡萄牙语) 和模型架构的LLM功能.
主要方法:
- 四个LLM (GPT-3.5,GPT-4,Llama-2-7b-chat,Llama-2-70b-chat) 在英语,德语和葡萄牙语的临床叙述中进行了测试.
- 临床缩写意义库存 (CASI) 用于英语,以及德国和葡萄牙的自定义数据集.
- 对LLM输出进行了准确性分析,以消除缩写的模糊性,考虑语言和模型类型.
主要成果:
- GPT-4表现出卓越的性能,在英语CASI数据集上达到0.98准确度.
- 从英语到德语和葡萄牙语的LLM成绩显著下降.
- 拉玛模型表现出有限的成功,特别是在德国和葡萄牙的临床文本中失败.
结论:
- 推GPT-4用于英语临床缩写解读,以提高患者和专业人员的文本清晰度.
- 目前的LLM,包括Llama模型,需要进一步改进,以便在德国和葡萄牙的临床叙述中有效地消除缩写的模糊性.
- 尽管Llama模型具有在本地敏感数据处理的潜力,但它还不适合缩写解决任务.
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