适应电子健康记录应用程序的生成性大语言模型的性能和改进策略:系统审查
Xinsong Du1, Zhengyang Zhou2, Yifei Wang2
1Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA 02115, United States; Department of Medicine, Harvard Medical School, Boston, MA 02115, United States.
International journal of medical informatics
|August 30, 2025
概括
对电子健康记录 (EHR) 分析进行生成性大语言模型 (LLM) 的调整是有希望的,但性能改进策略需要进一步的定量评估和有效和安全应用的明确准则.
科学领域:
- 生物医学信息学
- 医疗保健中的人工智能
- 健康数据科学
背景情况:
- 生成大型语言模型 (LLM) 提供了分析电子健康记录 (EHR) 的潜力.
- 适应EHR数据需要有效的绩效和改进策略.
- 需要对现有战略及其评估影响进行系统综合.
研究的目的:
- 综合性能和改进策略,以适应EHR分析中的生成LLM.
- 确定主要的临床应用,并总结报告的性能评估.
主要方法:
- 按照PRISMA指南进行系统审查 (PubMed,科学网络,2023年1月 - 2024年11月).
- 包含使用生成式LLM进行现实世界EHR数据分析的研究,并进行定量性绩效评估.
- 审查人员包括生物医学信息专家和临床医生.
主要成果:
- 196项研究符合标准,57. 1%专注于临床决策支持,20 4%专注于文档.
- 大多数研究 (88.8%) 没有对改善策略进行定量评估.
- 几次拍摄的提示,微调和多模式集成显示出混合的结果;较小的微调模型有时会超过较大的模型.
结论:
- 在EHR中使用LLM的绩效改进策略并不能保证更好的结果.
- 需要详细的,基于证据的指导方针来有效和安全地实施这些战略.
- 进一步的定量分析对于制定这些指南至关重要.
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