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预测尿路感染的机器学习算法:人口统计数据和杆反射结果的整合
Julien Favresse1,2, Julien Cabo1, Maxime Bosse1
1Department of Laboratory Medicine, Clinique St-Luc Bouge, Namur, Belgium.
Clinical chemistry
|August 26, 2025
概括
机器学习模型可以快速预测尿路感染 (UTI),从而减少不必要的抗生素使用. 与传统方法相比,CatBoost分类器显示出更快的尿路感染诊断.
科学领域:
- 泌尿外科
- 传染性疾病
- 医疗信息学
背景情况:
- 尿路感染很常见,传统的培养诊断需要24-48小时.
- 尿液培养的高百分比 (70-80%) 是阴性,导致过度使用抗生素.
- 快速识别阴性尿路感染病例对于优化抗生素管理至关重要.
研究的目的:
- 开发和评估用于快速预测尿道感染的机器学习模型.
- 将ML模型的性能与传统测量棒尿液分析参数进行比较.
主要方法:
- 分析了22,961名患者的尿样.
- 开发和评估使用17个预测参数的6个ML模型,包括测量结果和人口统计数据.
- 数据分为70%的培训和30%的独立测试集.
主要成果:
- CatBoost 分类器获得了最高的性能,ROC 曲线下的面积为92.0%-94.7%.
- 该模型的负预测值始终高于95%,平均精度从68.2%到81.6%.
- 传统的测量参数 (酸盐和白细胞雌激酶) 的预测性能明显较低.
结论:
- 机器学习模型,特别是CatBoost分类器,提供了非常准确和快速的尿路感染诊断工具,在不到一个小时内提供结果.
- 这些模型可以帮助临床医生及时做出诊断决定.
- 建议对抗生素处方的影响进行进一步的验证和研究.
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