机器学习预测急诊室的细菌性尿
Johnathan M Sheele1,2, Ronna L Campbell3,4, Derick D Jones3,4
1Department of Emergency Medicine (Sheele), Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA. sheele.johnathan@mayo.edu.
Scientific reports
|August 24, 2025
概括
机器学习使用急诊室数据准确预测尿路感染. 在等待培养结果时,这可以帮助指导抗生素治疗决策.
科学领域:
- 医疗信息学
- 计算生物学
- 传染性疾病
背景情况:
- 尿路感染很常见,但往往被错误诊断和治疗.
- 准确及时诊断尿路感染对于有效治疗患者至关重要.
研究的目的:
- 评估机器学习模型,使用现有的急诊室 (ED) 数据来预测细菌性尿.
- 评估各种机器学习算法在尿液培养中的不同细菌生长值的预测性能.
主要方法:
- 对62,963例ED接触的回顾分析与尿液分析和尿液培养数据 (2017-2021年).
- 逻辑回归,k-最近邻居,随机森林,极端梯度增强 (XGBoost) 和深度神经网络的比较.
- 三种尿液培养结果的预测:任何微生物生长,≥10,000 CFU/mL和≥100,000 CFU/mL.
主要成果:
- XGBoost 的预测准确度最高,分别为 86. 1%, 89. 1% 和 93. 1%.
- 在被诊断为尿路感染前培养的病例中,XGBoost在预测没有生长或≥100,000 CFU/ ml时获得了91%的AUROC.
- 该模型仅使用ED遭遇期间可用的数据准确预测细菌性尿.
结论:
- 机器学习,特别是XGBoost,可以使用例行收集的ED数据准确预测细菌性尿.
- 这些算法为临床医生提供了有价值的工具来预测培养结果,并告知实证抗生素治疗决策.
- 将机器学习纳入临床工作流程可以改善疑似尿道感染的管理.
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