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基于机器学习的风险评估工具,以排除ESBL向治疗在特有地区的实证使用

H D Ravkin¹, R M Ravkin², E Rubin³

¹Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.

The Journal of hospital infection

|April 28, 2024

概括

一个新的机器学习工具有助于识别不太可能患有扩展谱β-乳糖酶 (ESBL) 耐药性的患者. 这种抗菌药物管理策略减少了在高耐药性地区不必要的ESBL向治疗.

科学领域:

传染性疾病传染性疾病
临床微生物学临床微生物学
医疗信息学医疗信息学

背景情况:

抗菌药物管理需要确定需要扩展谱β-乳糖酶 (ESBL) 向治疗的患者.
现有的"规则"工具对于高患病率的ESBL地区是不够的,导致过度选择患者.

研究的目的:

开发基于机器学习的"排除"工具,用于识别不需要ESBL向治疗的患者.
创建一个适合在抗菌素耐药性高的地区临床使用的工具.

主要方法:

一个风险预测模型使用渐变增强的决策树在17913名大肠杆菌和Klebsiella pneumoniae患者的数据上进行了训练.
使用Shapley值来评估各种预测变量的贡献.

主要成果:

该模型实现了0.72的接收器操作特征曲线下的面积,用于识别ESBL流行地区的低风险患者.
选择具有最低预测风险的30%患者的结果是负预测值至少为0.74.
一个简化的7个特征模型表现出与完整模型可比的性能,并且可以作为Web应用程序使用.

结论:

抗生素耐药性的风险计算器是一种可行的"排除"策略,以减少ESBL向治疗的使用.
简化模型的强大性能支持其在ESBL高流行地区的临床采用.
这种工具为广泛的实证卡巴胺使用提供了替代方案,因为ESBL产生病原体的比例不断上升.

关键词:

抗生素管理的管理.抗微生物耐药性抗微生物耐药性人工智能的人工智能是人工智能.数据科学是数据科学.扩展光谱β-lactamase是一种扩展光谱β-lactamase.医疗信息学医学信息学

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