在膀癌患者的泌尿器切除术后预测90天尿路感染的风险,使用机器学习和可解释性
Qi Zhao1, Meng-Yao Liu2, Kai-Xia Gao1
1Department of Urinary Surgery, The First Hospital of Shanxi Medical University, Taiyuan, 030000, Shanxi Province, China.
Scientific reports
|February 25, 2025
概括
一个新的机器学习模型预测尿道感染 (UTI) 风险在泌尿器切除术后膀癌患者. SVM模型实现了高准确度,有助于早期检测和管理泌尿器官切除术后的泌尿器官感染.
科学领域:
- 在瘤学瘤学.
- 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
背景情况:
- 尿路感染 (UTI) 是膀癌患者的泌尿器切除术后的一个重要并发症.
- 预测泌尿切除术后的尿路感染对于及时干预和改善患者结果至关重要.
研究的目的:
- 设计和验证一种机器学习模型,用于预测尿路感染在泌尿器切除术后90天内发生的概率.
- 在这个患者队列中确定与尿路感染风险相关的关键临床特征.
主要方法:
- 分析了317名经过泌尿器切除术的膀癌患者的临床和随访数据.
- 应用最小绝对收缩和选择运算符 (LASSO) 回归来进行特征选择.
- 评估七个机器学习算法,包括支持矢量机 (SVM),以AUC,精度和F1得分等性能指标.
- 为了模型的可解释性,利用了夏普利的添加式解释 (SHAP) 和局部可解释的模型不可知解释 (LIME).
主要成果:
- 在泌尿器切除术后90天内,观察到显著的22.08%的尿路感染发病率.
- 该预测模型确定了八个重要的临床特征,这些特征有助于尿路感染风险.
- SVM模型表现出优异的性能,AUC为0.835,准确度为0.825,F1得分为0.667.
结论:
- 开发的机器学习模型,特别是SVM实现,有效地预测膀癌患者的尿路感染风险.
- 该工具可以帮助医疗保健专业人员评估和管理尿路感染风险,从而减少并发症.
- 预测模型的在线版本可供临床使用.
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