开发和验证模型,用于术前预测风险和术后检测非传染性并发症,使用可解释机器学习和电子健康记录数据
Adam Dyas1, Christina M Stuart1, Yizhou Fei2
1Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado Anschutz Medical Campus.
Annals of surgery
|March 26, 2025
概括
新的机器学习模型使用电子健康记录数据预测和检测非传染性术后并发症. 这些工具可以增强患者监测和改善外科手术结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 手术结果研究研究.
背景情况:
- 之前的工作建立了预测手术部位感染的模型.
- 这项研究的重点是为非传染性并发症开发类似的预测和检测模型.
研究的目的:
- 创建和验证可解释的机器学习模型,用于估计手术前风险.
- 利用电子健康记录 (EHR) 数据开发用于检测非传染性术后并发症的模型.
主要方法:
- 利用了来自五家医院 (2013-2019) 的30639名患者的EHR数据.
- 在开发风险和检测模型时使用拉索和敲击过器进行变量选择.
- 模拟的30天非传染性结果,包括死亡率,发病率 (整体,出血,心脏,肺,脏,VTE),再录取和出院处置.
主要成果:
- 模型表现出强的表现:风险模型的曲线下面面积 (AUC) 范围为0.68-0.91,检测模型为0.92-0.97.
- 确定了每个模型的6-22个关键预测变量,确保节.
- 观察到的并发症率各不相同,总体发病率为10.4%,中风为0.1%.
结论:
- 开发了简洁,高性能的模型来估计风险和检测术后非传染性并发症.
- 这些模型的性能表明,有可能增强当前的手动监控方法.
- 这些可解释的机器学习工具为改善手术后患者安全提供了有希望的方法.
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