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自然语言处理以识别90天或更年轻的婴儿在呈现前发烧
Paul L Aronson1, Nathan Kuppermann2,3, Prashant Mahajan4
1Section of Pediatric Emergency Medicine, Departments of Pediatrics and of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut.
一个新的自然语言处理 (NLP) 算法使用电子健康记录准确识别发烧的婴儿. 这种工具通过在大型研究中找到婴儿前访问温度来增强研究.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床研究信息学 临床研究信息学
背景情况:
- 准确识别发烧的婴儿对于研究至关重要.
- 电子健康记录 (EHR) 包含有价值但非结构化的数据.
- 自然语言处理 (NLP) 提供了提取临床信息的潜力.
研究的目的:
- 改进和验证一种NLP算法,用于识别年幼发烧婴儿的量化温度测量.
- 从EHR中提取预急诊部 (ED) 发烧数据,用于研究队列识别.
主要方法:
- 基于规则的NLP算法使用17个EDS的EHR数据开发和完善.
- 这项研究包括了90天或更年轻的婴儿,在2012年至2023年期间进行了访问.
- 算法性能在500次访问的样本上得到验证,将NLP提取的温度与记录的临床笔记进行比较.
主要成果:
- 该NLP算法实现了高精度:95%的灵敏度和96%的特异性在确定量化温度测量时.
- 积极的预测值为86%.
- 调整温度计时稍微降低了灵敏度,但保持了高特异性.
结论:
- 一个经过验证的NLP算法可以有效地识别发烧的婴儿,使用ED前的温度数据.
- 该工具可为使用EHR数据的研究研究提供更全面的队列识别.
- 该算法有助于在大规模研究中在ED中包括没有记录发烧的婴儿.
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