使用GPT模型从临床笔记中提取社会决定因素和家族史的最低指令,零射击学习
Neel Jitesh Bhate1, Ansh Mittal1, Zhe He2
1Department of CIS, IUPUI, Indianapolis, IN, USA.
概括
这项研究探讨了使用GPT-3.5从临床笔记中提取患者人口统计,健康的社会决定因素和家庭病史的零射击. 取得了有希望的结果,特别是在人口统计学方面,通过微调有改善的潜力.
科学领域:
- 在医疗保健中的自然语言处理 (NLP)
- 临床信息学 临床信息学
- 人工智能 (AI) 在医学中的应用
背景情况:
- 电子健康记录 (EHR) 包含有价值的非结构化数据,如人口统计,健康的社会决定因素 (SDOH) 和家庭史.
- 通过将其与结构化数据相结合,提取这些信息对于改善医疗保健结果至关重要.
- 现有的研究经常使用监督学习;这项研究探讨了零射击方法.
研究的目的:
- 通过使用GPT模型来从临床笔记中提取人口统计,SDOH和家族史来研究零射击学习的有效性.
- 为了评估GPT-3.5的性能,对此信息提取任务的提示是最小的.
- 用传统的命名实体识别 (NER) 和语义相似度指标来评估提取精度.
主要方法:
- 使用非识别的,现实世界的临床笔记,对人口统计,SDOH和家族史进行注释.
- 在零射击学习环境中应用GPT-3.5,为模型提供最小的信息.
- 采用传统的NER指标 (例如F1得分) 和语义相似度指标进行全面评估.
主要成果:
- 在人口提取方面,GPT-3.5实现了高性能 (0.975 F1得分).
- 在提取健康的社会决定因素 (0.615 F1得分) 和家族史 (0.722 F1得分) 中表现适度.
- 通过案例研究确定了局限性,突出了未来研究领域.
结论:
- 使用GPT-3.5的零射击学习显示出从非结构化的临床文本中提取关键患者信息的潜力.
- 性能因信息类型而异,人口统计数据的提取非常准确.
- 预计通过微调或少量学习方法可以进一步改进,并确定了需要未来调查的局限性.
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