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预测皮肤穿神经切除术 (PCNL) 患者的操作时间:一种机器学习方法

Owais Ghammaz¹, Rami Alazab², Nabil Ardah¹

¹Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.

|May 17, 2025

概括

实现无石头状态显著减少皮肤穿神经切除术 (PCNL) 操作时间. 石头负担和性别也是程序长度的关键预测因素,根据机器学习模型的识别.

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
手术结果研究研究.

背景情况:

穿皮石切除术 (PCNL) 是一种常见的石切除手术.
预测和理解影响PCNL操作时间的因素对于手术规划和资源分配至关重要.

研究的目的:

为了确定影响皮肤瘤切除术 (PCNL) 程序持续时间的关键因素.
开发和评估用于预测PCNL运行时间的机器学习模型.

主要方法:

这是一项对352名接受PCNL.患者的回顾性队列研究.
利用机器学习算法 (随机森林,AdaBoost,XGBoost,物流回归) 来预测操作时间.
采用数据标准化和SMOTE用于不平衡的数据,以80/20的火车测试分成.

主要成果:

石头负担,性别和水解与较长的PCNL运行时间有显著的相关性.
机器学习模型确定了无石头状态,石头负担和性别作为主要预测因素.
极端渐变增强分类器显示了最高的预测性能 (AUC = 0.789).

结论:

没有石头的状态,石头负担和性别是PCNL程序长度的关键决定因素.
实现无石头状态与显著减少的操作时间有关.
机器学习,特别是XGBoost,为手术规划和优化患者结果提供了宝贵的预测能力.

关键词:

通过皮肤进行神经质切除术.机器学习是机器学习.运行时间运行时间

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