机器学习算法在尿路感染中的应用 基于非微生物参数的诊断
M Mar Rodríguez Del Águila1,2, Antonio Sorlózano-Puerto2,3,4, Cecilia Bernier-Rodríguez5
1Servicio de Medicina Preventiva y Salud Publica, Hospital Universitario Virgen de las Nieves, 18014 Granada, Spain.
Pathogens (Basel, Switzerland)
|October 29, 2025
概括
人工智能现在可以使用非微生物数据预测尿路感染 (UTI). 机器学习模型准确地识别尿路感染,可能减少需要耗时的尿液培养的需要.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的人工智能
- 临床微生物学 临床微生物学
背景情况:
- 尿路感染 (UTI) 非常普遍,特别是在女性和住院患者中.
- 目前的尿路感染诊断依赖于临床症状和尿液培养,这些都是耗时和资源密集的.
- 优化尿路感染检测对于及时的患者管理和资源分配至关重要.
研究的目的:
- 开发和评估人工智能 (机器学习) 模型,用于预测阳性尿液培养.
- 用非微生物实验室参数来加快尿路感染诊断.
- 减少不必要的尿液培养,提高诊断效率.
主要方法:
- 来自怀疑尿路感染的患者的4283种尿液培养物的分析 (2016-2020年).
- 应用各种机器学习算法,包括随机森林和树,来预测尿路感染和微生物类型.
- 从非微生物学测试中确定关键的预测变量.
主要成果:
- 随机森林获得了82.2%的准确性和87.1%的AUC来预测阳性尿液培养.
- 树算法准确地预测了79.0%的准确性.
- 关键预测因素包括尿液测量棒 (白细胞,亚酸盐),白细胞计数,单细胞计数,淋巴细胞百分比和肌素水平.
结论:
- 与非微生物学参数集成的AI算法显示出优化尿路感染诊断的重大前景.
- 机器学习模型可以有效预测尿路感染,可能减少对传统尿液培养的依赖.
- 在广泛整合医院实践之前,需要进一步的临床验证.
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