准备床边优化产后抑郁风险预测模型,用于卫生系统的临床实施
Yifan Liu1, Rochelle Joly2, Meghan Reading Turchioe3
1Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States.
概括
开发了一种机器学习模型,使用电子健康记录 (EHR) 预测产后抑郁症 (PPD). 在评估了性能和公平性后,创建了一个基于数据的模型,以改善临床决策支持.
科学领域:
- * 计算医学和健康信息学.
- * 临床信息学和机器学习在医疗保健中的应用.
背景情况:
- *产后抑郁症 (PPD) 是一个重大的公共卫生挑战.
- *电子健康记录 (EHR) 为开发预测模型提供了丰富的数据来源.
- *将预测模型集成到临床工作流程中需要严格的评估.
研究的目的:
- * 开发和外部验证用于使用EHR数据进行PPD预测的机器学习模型.
- * 在实施前评估模型的性能,公平性和临床适当性.
- *使用 debiasing 技术来完善模型,以确保公平的性能.
主要方法:
- *利用来自学术医疗中心和临床研究网络 (2014-2020) 的EHR数据.
- *使用曲线下面积和灵敏度评估预测性表现;评估具有差异影响的公平性,机会平等和预测性平价指标.
- * 采用决策曲线分析和专家评估临床适用性;比较了五种退化方法.
主要成果:
- *基线PPD模型在学术医疗中心数据中显示了一些不公平,但在临床研究网络数据中显示了公平的表现.
- *采用了"盲目公平" (debiasing) 的方法,提高了整体绩效和公平性.
- * 专家审查人员证实了修订模型的临床适用性.
结论:
- *对预测模型进行彻底的实施前评估至关重要,包括性能,公平性和临床适当性.
- *除偏技术可以减轻医疗保健机器学习模型中的公平问题.
- * 精细的PPD预测模型显示了EHR系统中临床决策支持的前景.
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