构建和验证可解释的XGBoost机器学习模型,以根据尿液分析数据预测ESBL阳性率
Lulu Zhang1, Shaokui Hua2, Yu Zhang3
1Department of Urology, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China.
概括
这项研究开发了一种机器学习模型,使用常规尿检数据快速识别扩展谱β-乳糖酶 (ESBL) 阳性尿路感染,改善患者的治疗结果并防止败血症的进展.
科学领域:
- 医疗信息学 医疗信息学
- 临床微生物学 临床微生物学
- 医疗保健中的机器学习
背景情况:
- 由扩展光谱β-乳糖酶 (ESBL) 生产生物体引起的尿路感染 (UTIs) 构成了重大的临床挑战.
- 传统的培养和药物敏感性测试对于尿路感染有很长的周转时间,延迟适当的治疗和增加败血症风险.
研究的目的:
- 开发和验证一个高效的机器学习 (ML) 模型,以快速识别尿路感染患者的ESBL阳性.
- 利用常规尿液分析数据来早期检测产生ESBL的细菌.
主要方法:
- 对528个尿样进行了回顾性研究.
- 拉索回归选变量,并使用70%的数据构建了9个ML模型.
- 基于性能指标 (AUC,精度,灵敏度,特异性) 选择了XGBoost模型,并使用十倍交叉验证和单独的测试集进行了验证. 用SHAP分析来确定模型的可解释性.
主要成果:
- 拉索回归确定了关键预测因素:性别,尿蛋白,乌罗基诺基因,白细胞,隐性血液,年龄,pH值,特异重量和各种细胞计数.
- XGBoost模型表现出高性能,在验证组中达到0.924的AUC,在测试组中达到0.968.
- 最终的模型在验证组中报告了0.862的精度,在测试组中报告了0.943的精度,具有高灵敏度和PPV.
结论:
- 成功开发了一种快速有效的ML模型,用于识别尿路感染中的ESBL阳性.
- 该模型利用易于获得的常规尿检数据,比传统方法有了显著的改进.
- 这种方法有可能加快治疗决策,缓解败血症的进展.
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