QAMT:一个基于LLM的框架,用于质量保证的医疗时间序列数据生成
Yi Luo1,2, Yong Zhang2, Chunxiao Xing2
1School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了QAMT,这是一种使用大型语言模型 (LLM) 来生成高质量,可解释的医疗时间序列数据的新框架. 通过确保数据质量和保持生成过程的透明度,QAMT解决了现有方法的局限性,以改善医学研究.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数据科学数据科学数据科学
背景情况:
- 现实世界医学时间序列数据对于研究和临床决策至关重要,但面临着诸如数量有限,质量差和隐私问题等挑战.
- 现有的数据生成方法,包括生成对抗网络 (GAN) 和变异自编码器 (VAE),难以应对医疗数据的复杂性,特别是静态事件数据,并且往往缺乏可解释性.
- 大型语言模型 (LLM) 是有前途的,但在生成时间数据和特定领域细微差别方面遇到了困难.
研究的目的:
- 提出QAMT,这是第一个基于LLM的框架,用于模块化生成质量可靠和可解释的医疗时间序列数据.
- 克服现有方法在生成高质量的静态和时间医学数据方面的局限性.
- 增强合成医疗数据的实用性,用于下游任务,如医学研究和临床决策.
主要方法:
- 开发了QAMT,一个模块化框架,利用LLM来生成医疗时间序列数据.
- 构建了一个健康知识图表,以赋予LLM医疗专业知识.
- 为同时生成静态事件和时间数据而设计的双模块,包含质量保证模块.
主要成果:
- 与现有方法相比,QAMT成功生成了医疗时间序列数据,质量得到了提高.
- 该框架确保了数据生成过程的可解释性,这是相对于传统方法的关键优势.
- 实验结果验证了QAMT在产生可靠的合成医学数据方面的有效性.
结论:
- 在使用LLMs生成高质量,可解释的医学时间序列数据方面,QAMT代表了显著的进步.
- 知识图和质量保证模块的模块化设计和集成解决了合成医疗数据生成的关键挑战.
- QAMT为增强现实世界的医疗数据提供了一个有前途的解决方案,从而支持医学研究和临床实践的进步.
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