TriMedPrompt:一个统一的提示框架,以实现现实和符合布局的临床进展记录综合
Garapati Keerthana1, Manik Gupta1
1Birla Institute of Technology and Science, Department of Computer Science and Information Systems, Hyderabad, 500078, India.
Journal of biomedical informatics
|October 19, 2025
概括
本研究介绍了TriMedPrompt,这是一种使用大语言模型 (LLM) 来生成合成临床进展记录的新框架. 这些现实的注释增强了保护隐私的临床自然语言处理 (NLP) 模型开发.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床自然语言处理 (NLP)
- 医疗保健中的人工智能
背景情况:
- 临床进展记录对于患者轨迹建模和临床决策审计至关重要.
- 公共进展说明的有限可用性阻碍了强大的机器学习模型开发.
- 像MIMIC-III这样的现有资源为全面的临床NLP研究提供了不足的数据.
研究的目的:
- 提出一种新的混合提示框架,TriMedPrompt,用于生成高质量的合成临床进展记录.
- 通过对各种生物医学信号进行条件化,利用大型语言模型 (LLM).
- 扩大对保护隐私的研究和开发现实的临床笔记的访问.
主要方法:
- 开发了TriMedPrompt,这是一个混合提示框架,集成了MIMIC-III笔记,PMC案例报告和PrimeKG结构化知识.
- 设计了一个多源,布局意识的提示管道,用于动态信息集成.
- 使用基于嵌入的检索来选择临床一致的病例报告.
- 在标准临床格式 (SOAP,BIRP,PIE,DAP) 中生成笔记.
主要成果:
- 生成的合成笔记实现了98.6%的语义实体与真实临床笔记的对齐.
- 在各种临床格式的生成笔记中保持高结构保真.
- 废弃研究证实了结合结构化知识和非结构化叙事数据的重要性.
- 证明了合成笔记在保护隐私的临床NLP中的潜力.
结论:
- TriMedPrompt有效地生成高质量的,结构和语义上一致的合成临床进展记录.
- 混合方法,结合不同的数据源,显著提高合成笔记质量.
- 这种可扩展的范式通过增加对现实数据的访问来推进可信的临床NLP研究.
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