使用生成性人工智能构建合成数据集,以训练大型语言模型,从临床笔记中对急性功能衰竭进行分类
Onkar Litake1, Brian H Park1, Jeffrey L Tully1
1Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA 92037, United States.
概括
合成临床笔记有效训练语言模型用于急性功能衰竭检测,匹配真实数据的性能. 这表明受保护的健康信息可能不必用于训练人工智能模型.
科学领域:
- 医疗信息学医学信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 临床笔记对于训练医疗保健中的AI模型至关重要.
- 由于隐私问题,访问真实的临床笔记往往受到限制.
- 合成数据生成为数据稀缺提供了一个潜在的解决方案.
研究的目的:
- 为了评估在合成临床笔记上训练的语言模型分类器的性能,与真实的笔记相比,用于识别急性功能衰竭.
- 为了确定合成数据是否可以在医疗AI应用中与现实世界的数据取得可比的结果.
主要方法:
- 使用语言模型开发了一个分类器来检测急性功能衰竭.
- 四个训练数据集进行了比较:真实的临床笔记 (MIMIC-III) 和三组由ChatGPT生成的合成笔记 (15,30和45句).
- 通过使用MIMIC-III测试组上的接收器操作特征曲线 (AUC) 下的面积来评估性能.
主要成果:
- 在真正的MIMIC-III笔记上训练时,RoBERTa模型实现了0.84的AUC.
- 使用ChatGPT生成的合成音符进行训练,导致AUC为0.80 (GPT-15),0.84 (GPT-30) 和0.76 (GPT-45).
- 合成数据的性能与真实数据的性能非常接近,特别是GPT-30集.
结论:
- 在合成临床笔记上训练的语言模型表现与在真实笔记上训练的语言模型相似,用于检测急性功能衰竭.
- 这些发现表明,合成数据可以成为真实的临床笔记的可行替代品,可能减少人工智能模型培训中对受保护健康信息的需求.
- 这项研究支持使用生成人工智能来创建训练数据集,为更容易访问和保护隐私的医学人工智能开发铺平了道路.
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