Extracting antibiotic susceptibility from free-text microbiology reports using natural language processing.

IF 2.9 4区 医学 Q2 INFECTIOUS DISEASES
Andrew Chou, Ronald George Hauser, Lori A Bastian, Cynthia A Brandt, Barbara W Trautner
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引用次数: 0

Abstract

There is a clinical need to appropriately apply large language model (LLM)-based systems for use in infectious diseases. We sought to use LLM and machine learning for extracting antibiotic susceptibility from clinical microbiology free-text reports, allowing use for outbreak detection, increasing information gathering efficiency, and public health reporting.

使用自然语言处理从自由文本微生物学报告中提取抗生素敏感性。
临床需要适当地应用基于大语言模型(LLM)的系统用于传染病。我们试图使用LLM和机器学习从临床微生物学自由文本报告中提取抗生素敏感性,允许用于爆发检测,提高信息收集效率和公共卫生报告。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.40
自引率
6.70%
发文量
289
审稿时长
3-8 weeks
期刊介绍: Infection Control and Hospital Epidemiology provides original, peer-reviewed scientific articles for anyone involved with an infection control or epidemiology program in a hospital or healthcare facility. Written by infection control practitioners and epidemiologists and guided by an editorial board composed of the nation''s leaders in the field, ICHE provides a critical forum for this vital information.
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