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使用基本的人口和临床特征的机器学习模型来识别有子宫切除史的妇女的尿失禁:一个横截面研究

Lu Liu¹, Wei Chen², Lili Li¹

¹Department of Obstetrics and Gynecology, The Second Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China; Shandong Key Laboratory of Cancer Digital Medicine, Jinan, China.

International journal of medical informatics

|February 11, 2026

概括

机器学习模型有效地识别了子宫切除术后妇女的尿失禁 (UI). 这种方法,使用名ograms和在线工具,改善了风险识别的妇女有过子宫切除术的历史.

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
医疗保健中的机器学习

背景情况:

尿失禁 (UI) 是一个重要的健康问题,妇女有过子宫切除的历史.
澄清良性原因的子宫切除和UI之间的联系对于防止不必要的手术至关重要.

研究的目的:

开发一种机器学习 (ML) 模型,用于识别与宫切除术后女性UI相关的因素.
创建一个可解释和可访问的临床决策支持工具.

主要方法:

来自NHANES数据库的2021患者的分析,用于模型导出.
使用6个ML算法 (LR,NB,MLP,XGBoost,RF,SVM) 对13个人口和临床特征的评估.
对556名患者进行外部验证;通过名图和SHAP进行解释;作为在线平台进行部署.

主要成果:

机器学习模型在内部和外部验证队列中显示出一致的预测性能.
曲线下的面积 (AUC) 值在0.702到0.763之间,精度在0.627到0.697.7之间.
在测试的ML算法中没有观察到显著的性能差异.

结论:

ML模型有效地识别了在有过子宫切除病史的女性中的UI.
开发的名录和在线工具提高了识别有风险的妇女的可访问性和可行性.
这种方法支持了关于子宫切除术和UI管理的知情临床决策.

关键词:

人口和临床特征.经过子宫切除术的历史机器学习机器学习尼汉斯 (NHANES) 是一个名人.在这里,我们可以看到UIUIUIUI.

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