来自荷兰全科医生笔记的文本在预测建模中的附加值
Tom M Seinen1, Jan A Kors1, Erik M van Mulligen1
1Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Journal of the American Medical Informatics Association : JAMIA
|August 16, 2023
概括
非结构化的电子健康记录数据可以在一般实践中创建有效的预后模型. 结合文本和结构化数据,进一步提高模型性能,增强患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 临床预测建模模型
- 自然语言处理自然语言处理.
背景情况:
- 一般医生 (GP) 的电子健康记录 (EHR) 包含有价值的非结构化的临床笔记.
- 开发准确的预后预测模型对于改善初级保健机构的患者护理至关重要.
研究的目的:
- 评估荷兰非结构化EHR数据的实用性,以及结构化数据,用于构建预后预测模型.
- 用文本数据,结构化数据或两者的组合来评估模型的性能.
主要方法:
- 训练并验证了84个预测模型,用于4个临床问题,使用各种文本表示和算法.
- 利用GP EHR观察数据,在不同的EHR系统中进行内部和外部验证.
主要成果:
- 仅使用文本数据的模型在两个预测任务中表现与结构化数据相比或比结构化数据更好.
- 将结构化数据和文本数据结合起来,在这些任务中始终优于单独使用任何数据类型的模型.
- 在不同的文本表示和算法中观察到的最小性能变化.
结论:
- 非结构化的EHR数据可以独立产生表现良好的临床预测模型.
- 将非结构化数据与结构化数据集成为预测准确性提供了显著的附加值.
- 这项研究强调了非英语语言临床NLP和跨EHR系统验证的重要性.
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