在美国对比基于规则和机器学习的数字自我分类系统
Bilal A Naved1,2, Yuan Luo3
1Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Chicago, IL, USA.
NPJ digital medicine
|December 26, 2024
概括
本次审查分析了患者自我选系统,没有发现卫生系统发布的用于处理患者数据的大型语言模型 (LLM). 它检查了美国卫生系统网站上当前的自我选解决方案.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数字健康数字健康
背景情况:
- 几十年来,患者智能访问和自我选系统一直在发展.
- 专家系统和计算模型已被广泛部署.
- 卫生系统尚未发布用于处理自我报告的患者数据的大型语言模型 (LLM).
研究的目的:
- 审查患者自我选系统的进展情况.
- 分析美国卫生系统目前提供的自我分类解决方案.
- 为了确定在这个领域缺乏基于LLM的解决方案.
主要方法:
- 系统审查关于患者自我选的科学文献.
- 分析647个美国卫生系统网站上可用的自我选工具.
- 评估现有系统中使用的技术类型.
主要成果:
- 几十年来,患者接入和自我分类系统的发展.
- 专家系统和计算模型的广泛使用.
- 卫生系统没有发表基于LLM的自我选解决方案.
- 在647个美国卫生系统中识别和分类现有的自我选解决方案.
结论:
- 患者自我选领域经历了显著的发展,但缺乏集成先进的LLM技术.
- 现有的系统主要依赖于较旧的计算模型和专家系统.
- 需要进一步的研究和开发,以探索患者自我报告数据处理的LLM应用.
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