对比美国基于规则和机器学习的数字自我分诊系统。
Contrasting rule and machine learning based digital self triage systems in the USA.
作者信息
Naved Bilal A, Luo Yuan
机构信息
Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Chicago, IL, USA.
Department of Preventative Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
出版信息
NPJ Digit Med. 2024 Dec 27;7(1):381. doi: 10.1038/s41746-024-01367-3.
Patient smart access and self-triage systems have been in development for decades. As of now, no LLM for processing self-reported patient data has been published by health systems. Many expert systems and computational models have been released to millions. This review is the first to summarize progress in the field including an analysis of the exact self-triage solutions available on the websites of 647 health systems in the USA.
患者智能访问和自我分诊系统已经开发了几十年。截至目前，医疗系统尚未发布用于处理患者自我报告数据的大型语言模型。许多专家系统和计算模型已经发布给了数百万人。本综述首次总结了该领域的进展，包括对美国647个医疗系统网站上可用的精确自我分诊解决方案的分析。
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