基于电子健康记录的新诊断克罗恩病病例的识别
Susanne Ibing1, Julian Hugo2, Florian Borchert2
1Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA; Windreich Dept. of Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, USA.
Artificial intelligence in medicine
|November 26, 2024
概括
使用电子健康记录 (EHR) 识别早期克罗恩病 (CD) 患者是可行的. 纳入临床笔记显著改善了CD风险因素和指标的预测模型性能.
科学领域:
- 医疗信息学 医疗信息学
- 胃肠病学 胃肠病学
- 数据科学数据科学数据科学
背景情况:
- 早期诊断克罗恩病 (CD) 对于减少手术和并发症至关重要.
- 诊断延迟在临床实践中很常见,这凸显了需要改进的识别方法的需要.
- 电子健康记录 (EHR) 为了解CD风险因素和指标提供了宝贵的资源.
研究的目的:
- 使用EHR数据识别,描述和预测发生的克罗恩病 (CD) 患者.
- 评估基于结构化和非结构化电子健康记录数据的表型化算法的有效性.
- 评估不同建模方法和变量对预测性能的影响.
主要方法:
- 开发了使用结构化EHR数据 (诊断,药物,利用) 和非结构化临床笔记 (2011-2023) 的表型算法.
- 进行了废除研究,以评估使用各种模型,预测时间,数据输入,文本编码和匹配变量的分类性能.
- 通过手动图表审查验证已识别的队列.
主要成果:
- 确定了247例CD病例和1221个匹配的对照;通过图表审查验证了队列.
- 在诊断前至少180天的病例中,胃肠道症状显著过度表现.
- 添加基于文本的功能改善了模型性能;种族匹配比算法选择或输入数据的影响更大.
结论:
- 证明了在美国卫生系统中使用EHR识别新诊断的CD患者的可行性.
- 尽管使用最先进的方法和多样化的数据,预测建模实现了适度的性能.
- 纳入临床笔记 (结构化或非结构化) 有助于为CD创建队列和预测建模.
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