通过ChatGPT自动确定HEART分数:对代快速开发的框架进行磨练
Conrad W Safranek1, Thomas Huang1, Donald S Wright2
1Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven Connecticut USA.
Journal of the American College of Emergency Physicians open
|March 14, 2024
概括
这项研究引入了一个改善临床数据提取大语言模型 (LLM) 准确性的框架. 快速改进增强了自动化HEART得分的确定,显示了临床笔记分析的潜力.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 显示出分析临床笔记的前景.
- 准确提取临床数据对于患者护理至关重要.
- 自动化风险评分计算可以简化临床工作流程.
研究的目的:
- 提出一个设计框架,以提高临床笔记分析中的LLM准确性.
- 通过快速改进来证明框架的实用性,用于自动 HEART 评分的确定.
- 评估代提示设计对LLM在提取复杂临床信息方面的表现的影响.
主要方法:
- 开发了一个用于LLM快速测试的管道,使用随机重复测试.
- 对医生的评估进行量化响应错误.
- 在多个LLM查询轮中,对HEART分数子组件的代精细提示.
主要成果:
- 代提示设计减少了HEART子得分的错误非数值响应.
- 在数值HEART子评分响应中,GPT-4显示出更好的准确性,平均误差从0.16降至0.10.
- 该框架有助于提高LLM在提取和应用临床数据方面的性能.
结论:
- 在LLMs的临床应用中建立了代快速设计的框架.
- 证明了LLMs在结构化临床笔记分析方面的潜力.
- 强调需要对大规模,现实世界的临床数据进行验证,并采取隐私保护措施.
关键词:
聊天GPT 聊天 在GPT 聊天心脏分数的得分是什么?医学中的人工智能临床决策支持系统 临床决策支持系统临床笔记分析分析紧急部门的风险算法大型语言模型.自然语言处理自然语言处理.快速的工程迅速的工程更多相关视频
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