通过机器学习系统预测皮肤性瘤切除术的无石头状态
Rami AlAzab1, Owais Ghammaz2, Nabil Ardah2
1Department of General Surgery and Urology, King Abdullah University Hospital, Irbid, Jordan.
International journal of nephrology and renovascular disease
|September 18, 2023
概括
机器学习模型可以准确地预测皮肤穿神经切除术 (PCNL) 后的无石头状态,其表现优于传统的评分系统. 这些模型为PCNL患者的结果提供了更好的预测.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 穿皮石切除术 (PCNL) 是一种常见的石切除手术.
- 预测PCNL后的无石头状态 (SFS) 对患者管理和治疗成功至关重要.
- 现有的评分系统如S.T.O.N.E.等. 和Guy的石头得分在预测PCNL结果方面存在局限性.
研究的目的:
- 开发和评估用于预测PCNL患者SFS的机器学习模型 (MLM).
- 为了比较MLM的预测性能与已建立的评分系统 (S.T.O.N.E. 和盖伊的石头得分).
主要方法:
- 对320名PCNL患者的回顾性分析.
- 提取手术前和手术后的变量.
- 开发和验证三个MLM:随机森林分类器 (RFC),支持矢量机 (SVM) 和XGBoost.
- 使用平均启动步骤估计,十倍交叉验证,分类报告和曲线下的面积 (AUC) 的性能评估.
主要成果:
- 队列中的整体SFS为69.4%.
- 与S.T.O.N.E.相比,MLM显示出优越的预测性能. 和Guy的石头得分,具有更高的AUC值.
- 在内部验证中,RFC,SVM和XGBoost的AUC分别为0.761,0.751和0.769,在外部验证中分别为0.795,0.858和0.84.
结论:
- 机器学习模型可以准确地预测PCNL患者的SFS.
- 开发的MLM在预测PCNL结果的传统评分系统上提供了显著的改进.
- 在PCNL程序中,MLM显示出增强临床决策和患者分层的前景.
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