增加电子健康记录,以检测不良事件
medRxiv : the preprint server for health sciences
|February 23, 2026
概括
从电子健康记录 (EHR) 中预测不良事件 (AE) 是一个挑战. 我们的新TASER-AE数据增强方法通过解决EHR数据中的类不平衡,显著改善AE预测.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床数据分析 临床数据分析
背景情况:
- 医疗干预的不良事件 (AE) 会增加患者的发病率,死亡率和医疗费用.
- 使用电子健康记录 (EHR) 预测AEs对于及时干预至关重要,但受到数据挑战的阻碍.
- 经典的机器学习方法与不平衡的EHR数据,缺失的标签和复杂的相互作用作斗争.
研究的目的:
- 引入TASER-AE,这是一个用于结构化EHR数据的新型数据增强管道.
- 通过解决阶级不平衡和改善少数阶级代表性来提高不良事件的预测.
- 提高电子健康数据分类模型的稳定性和预测性能.
主要方法:
- 开发了TASER-AE,这是一个以自然语言处理 (NLP) 技术为灵感的数据增强管道,适用于结构化EHR数据.
- 使用基于变压器的分类模型与增强的EHR数据结合使用.
- 将管道应用于稀疏和不平衡的临床数据集,以丰富少数不良事件类.
主要成果:
- 塔塞尔-AE获得了少数类F1分数高达0.70,明显超过了经典机器学习基线.
- 在两个不同的EHR数据集中,在不良事件检测性能方面取得了实质性的改进.
- 有效地缓解了阶级不平衡,提高了少数不利事件类别的代表性.
结论:
- 结构化,NLP启发的数据增强方法可以克服临床预测建模中的数据限制.
- 通过增强的AE预测,TASER-AE显示了改善患者安全结果的巨大潜力.
- TASER-AE管道为研究人员提供了一个有价值的工具,用于处理不平衡的临床数据.
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