开发和验证一个风险预测模型的后手术尿在妇科腹腔骨盆外科手术后的保留
Jiahao Shi1, Youbei Lin1, Xiaojing Qin2
1Jinzhou Medical University, Jinzhou, China.
Scientific reports
|December 19, 2025
概括
机器学习准确地预测了妇科手术患者手术后的尿路保留风险. 渐变增强决策树 (GBDT) 模型确定了手术时间和血液损失等关键因素,有助于临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 手术瘤学手术瘤学
- 机器学习应用 机器学习应用
背景情况:
- 手术后的尿 (PUR) 是妇科腹腔盆腔手术后的常见并发症.
- 准确预测PUR对于及时干预和改善患者结果至关重要.
- 现有的预测方法可能缺乏复杂手术场景所需的精度.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测妇科腹腔盆腔手术后的PUR.
- 确定导致PUR发展的关键预测因素.
- 创建一个用户友好的在线计算器用于临床决策支持.
主要方法:
- 使用了12个机器学习算法,包括渐变增强决策树 (GBDT),用于模型开发.
- 采用LASSO回归来选择特征,确定了8个重要的预测因素.
- 应用SMOTENC过量采样和算法调整,使用AUC,精度,F1得分,特异性和回忆进行全面的模型评估.
主要成果:
- 确定了PUR的8个关键预测因素:年龄,更年期状态,手术方法,术后止痛,麻醉类型,POP-Q,手术时间和手术内流血.
- GBDT模型表现出高AUC值 (0.997训练,0.927测试,0.710外部验证) 的优异性能.
- SHAP分析强调了手术时间,手术期间流血,手术方法和年龄是最有影响力的因素.
结论:
- 机器学习模型,特别是GBDT,在预测妇科手术后PUR风险方面提供了显著的优势.
- 开发的在线计算器和SHAP分析为临床医生提供了有价值的工具来评估高风险患者.
- 这些发现支持加强早期查和干预策略,以预防术后尿.
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