开发一种机器学习方法,用于预测在单一机构中接受剖腹产的患者的红细胞输血
Sang-Wook Lee1, Bumwoo Park2, Jimung Seo3
1Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Pungnap 2(i)-dong, Songpa-gu, Seoul, 05505, Republic of Korea.
Scientific reports
|July 18, 2024
概括
机器学习,特别是XGBoost,可以准确地预测剖腹产 (CS) 期间的手术内输血. 模型性能在不同数据比率中强大,识别了像胎盘前和血红蛋白水平这样的关键预测因素.
科学领域:
- 产科和妇科 产科和妇科
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 剖腹产期间的手术内出血需要输血,尽管产科护理的进步.
- 预测需要输血对于患者安全和资源管理至关重要.
研究的目的:
- 确定最佳的机器学习模型,用于预测CS接受患者的手术内输血.
- 评估数据不平衡和重新采样比率对模型性能的影响.
主要方法:
- 评估了八种机器学习算法 (XGBoost,K-最近邻居,决策树,支持矢量机,多层感知子,后勤回归,随机森林,深度神经网络).
- 评估模型性能是使用接收器操作特征曲线 (AUROC) 下面的面积和精度回忆曲线 (AUPRC) 下面的面积.
- 使用了各种事件与非事件比率 (1:1, 1:2, 1:3, 1:4) 和原始数据的数据集.
主要成果:
- XGBoost表现出最高的诊断性能,AUROC为0.8257和AUPRC为0.4825.XGBoost的诊断性能最高,AUROC为0.8257和AUPRC是0.4825.XGBoost的诊断性能最高.
- 重要的预测因素包括胎盘前 (全和部分),血红蛋白和血小板.
- 模型性能在很大程度上不受数据重新采样比率变化的影响.
结论:
- XGBoost 是一个最佳的算法,用于预测母亲CS的手术内输血.
- 数据平衡技术并没有显著改善预测模型的性能.
- 准确的预测可以帮助在剖腹产期间主动管理输血需求.
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