从患者咨询到图表:利用患者旅程知识图表构建的LLM
Hassan S Al Khatib1, Sudip Mittal1, Shahram Rahimi2
1Mississippi State University, Starkville, MS, USA.
概括
患者旅程知识图 (PJKGs) 整合了各种健康数据,以便更好地协调护理. 大型语言模型 (LLM) 构建这些图表,改善患者结果预测和个性化见解.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 知识表示 知识表示
背景情况:
- 医疗保健行业正在向以患者为中心的模式转变,需要全面了解患者的旅程.
- 现有的医疗保健数据系统缺乏整体表示,阻碍了有效的护理协调.
- 患者旅程知识图 (PJKGs) 通过将各种患者信息统一到结构化格式中提供解决方案.
研究的目的:
- 展示使用大型语言模型 (LLM) 构建PJKG的方法.
- 展示PJKG如何在患者数据中捕捉时间和因果关系.
- 为改善医疗保健提供先进的推理和个性化见解.
主要方法:
- 利用LLM来处理临床文档和患者-提供者对话.
- 从非结构化和半结构化健康数据构建PJKG的方法.
- 评估了PJKG建设的四个LLM (Claude 3.5,Mistral,Llama 3.1,ChatGPT4o) 的使用情况.
主要成果:
- 所有评估的LLM在PJKG建设中都显示出完美的结构合规性.
- 在医疗实体处理,计算效率和语义准确性方面,在LLM中观察到显著的差异.
- 构建的PJKG有效地捕捉了临床事件之间的时间和因果关系.
结论:
- PJKG提供了一个强大的框架,用于整合不同的患者信息.
- 通过LLM驱动的PJKG建设,通过加强护理协调和结果预测,推动了以患者为中心的医疗保健.
- 需要进一步的研究来优化LLM的性能,以便在PJKG中对特定的医疗实体识别和语义准确性进行优化.
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