使用机器学习和电子健康记录数据进行术前预测术后感染
Yaxu Zhuang1,2, Adam Dyas1,3, Robert A Meguid1,3,4
1Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus.
这项研究使用电子健康记录开发了准确的模型,以预测术后感染风险. 这些工具可以有效地估计许多患者的感染率,提高手术质量.
科学领域:
- 医疗信息学 医疗信息学
- 改善外科手术的质量
- 预测分析在医疗保健中的应用
背景情况:
- 目前的手术后感染监测依赖于手动图表审查,这是昂贵和耗时的.
- 使用电子健康记录 (EHR) 数据的自动化方法显示了及时全面监测的潜力.
- 现有的电子健康记录模型缺乏针对术后感染率的特定风险调整.
研究的目的:
- 开发和验证使用结构化EHR数据对术后感染进行术前风险预测模型.
- 为了能够有效及时估计经风险调整的术后感染率.
- 为了增强感染监测的传统手动图表审查方法.
主要方法:
- 利用了5家医院 (2013-2019) 中30639名患者的手术前EHR数据.
- 将EHR数据与美国外科医生学院国家外科质量改进计划 (ACS-NSQIP) 数据进行了链接,以获得结果.
- 采用拉索和淘汰过器进行变量选择以识别关键预测因素.
主要成果:
- 开发了6-7个预测器的节模型,用于手术部位感染,尿路感染,败血症和肺炎.
- 关键预测因素包括手术前情况,伤口分类,并发症和ASA身体状况.
- 模型实现了高性能,接收器操作特征下面面积 (AUROC) 曲线从0.73到0.89.
结论:
- 开发了准确的,基于EHR的术前模型,用于预测术后感染风险.
- 这些模型表现出基于手动图表审查的ACS-NSQIP模型的可比性能.
- 这些模型提供了一个可扩展的解决方案,用于及时,风险调整的术后感染率估计.
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