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An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

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Published on: January 27, 2010

机器学习算法预测手术后出血并发症后侧面皮穿皮nephrolithotomy后的手术后出血并发症.

Rui Meng¹, Weining Wang¹, Zhipeng Zhai¹

¹Department of Urology, YuQuan Hospital, Tsinghua University, Beijing, China.

|January 26, 2024

概括

机器学习模型可以预测皮肤穿神经切除术 (PCNL) 后的出血. 随机森林模型表现最好,帮助泌尿科医生对脏和尿道结石的治疗决策.

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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
腎臟病學 (nephrology) 是一種醫學專業.

背景情况:

经外科手术后出血是皮肤透神经切除术 (PCNL) 后的一个重大风险.
准确预测出血并发症对于患者管理和手术规划至关重要.

研究的目的:

开发和评估机器学习模型,用于预测经过横向位后的术后出血. PCNL.
为了确定影响脏和上尿路结石PCNL患者出血风险的关键因素.

主要方法:

追溯收集了356名接受横向位PCNL的患者的数据.
使用物流回归,随机森林和极端梯度提升 (XGBoost) 开发预测模型.
使用准确度,精度,F1得分,ROC曲线和AUC评估模型性能.

主要成果:

12.07%的患者经历了术后出血并发症.
随机森林模型获得了最高的准确性 (74.5%) 和AUC (0.679).
后勤回归和随机森林模型展示了合理的预测能力.

结论:

机器学习模型,特别是Random Forest,可以有效地预测PCNL侧面位后的术后出血.
这些预测工具可以帮助泌尿科医生优化治疗策略和管理潜在的并发症.
这些模型还可以帮助预测PCNL后的石头残留和复发.