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使用可解释机器学习和电子健康记录数据对非传染性术后并发症的风险调整结果的估计

Christina M Stuart^1,2, Yizhou Fei³, Kathryn L Colborn^2,3,4,5

¹Department of Surgery, University of Colorado Anschutz Medical Campus.

Annals of surgery

|April 21, 2025

概括

使用电子健康记录 (EHR) 数据的统计模型准确地预测非传染性术后并发症,类似于手动图表审查. 这些模型可以改善外科手术质量倡议.

科学领域:

医疗信息学医疗信息学
改善外科手术的质量
医疗保健中的机器学习

背景情况:

术后并发症影响15%的住院手术.
目前的并发症报告严重依赖于手动图表审查.
术后感染的自动监测 (ASPIN) 项目已经扩展到包括非传染性并发症.

研究的目的:

将应用到电子健康记录 (EHR) 数据上的统计模型进行比较,以预测非传染性术后并发症.
评估ASPIN模型的性能,使用EHR数据与已建立的基准进行比较.

主要方法:

使用ASPIN模型对来自5家医院的EHR数据的非传染性并发症的预估术前和术后概率.
计算观察:预期 (O:E) 比率来评估模型性能.
在不同数据源 (ACS-NSQIP与EHR) 和患者队列中比较O:E比率.

主要成果:

对9种非传染性术后并发症的估计O:E比率.
在将ACS-NSQIP数据与EHR数据进行比较时,O:E比率的重叠置信区间 (98%).
这两种数据来源之间的异常状态达成一致 (78%).

结论:

根据EHR数据的机器学习获得的风险调整后术后结果与手动图表审查的结果非常相似.
基于EHR的模型可以有效地增强手动图表审查流程.
这些经过验证的模型提供了一个可扩展的方法来指导外科手术质量改进工作.

关键词:

欧洲人机数据 EHR数据发现并发症识别并发症复杂性预测复杂性预测可以解释的机器学习.术后并发症术后并发症风险调整风险的调整.

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