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Wan In Wei¹, Cyrus Lap Kwan Leung¹, Arthur Tang²

¹JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases

|November 10, 2023

概括

聊天GPT有效地将患者的症状叙述转化为结构化的标签,显示常见症状的高准确性和医学研究应用的潜力.

科学领域:

自然语言处理自然语言处理.
医疗信息学医疗信息学
医疗保健中的人工智能

背景情况:

患者的症状叙述对于诊断至关重要,但对结构具有挑战性.
将非结构化文本转换为结构化数据的自动化对于大规模分析至关重要.

研究的目的:

评估ChatGPT在将患者的症状叙述转化为标准化的症状标签方面的可行性和性能.
将ChatGPT (GPT-4和GPT-3.5) 的准确性与基于计算机的匹配算法进行比较,以提取症状.

主要方法:

从300个未被识别的COVID-19患者叙述中提取症状,使用标准算法和ChatGPT与零射击和少数射击提示.
将ChatGPT的精度 (灵敏度和特异性) 与标准方法进行比较.
评估了GPT-4和GPT-3.5.5之间的性能差异.

主要成果:

在零射击提示中,GPT-4表现出对所有症状的高特异性和对常见症状的高敏感性.
对不太常见症状的敏感性是中度的,在几次射击提示时得到改善.
在准确性和标签一致性方面,GPT-4的表现优于GPT-3.5.

结论:

聊天GPT显示承诺作为医学研究工具,用于将症状叙述转换为结构化的标签.
这种功能可以节省医疗研究数据编译的时间和精力,并提高症状检查器的准确性.
进一步的快速工程可以提高性能,积极影响医学研究和疾病爆发分析.

关键词:

聊天GPT 聊天在GPT 聊天实体的认可实体的认可大型语言模型.症状提取症状提取症状的叙述症状的叙述症状科学症状科学

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