在约旦,扩展光谱β-乳糖酶细菌和多药物耐药性使用新的机器学习系统进行预测
Enas M Al-Khlifeh1, Ibrahim S Alkhazi2, Majed Abdullah Alrowaily3
1Department of Medical Laboratory Science, Al-Balqa Applied University, Al-salt, 19117, Jordan.
Infection and drug resistance
|July 31, 2024
概括
机器学习模型有效预测细菌中的扩展谱β-乳糖酶 (ESBL) 和多药性耐药性 (MDR). 关键预测因素包括患者年龄和特定的抗生素类别,有助于优化抗生素治疗.
科学领域:
- 微生物学 微生物学
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 扩展光谱β-乳糖酶 (ESBL) 生产微生物的发病率不断上升,对公众健康构成了重大挑战.
- 机器学习 (ML) 越来越多地用于预测细菌抗生素耐药性,以优化治疗策略.
- 这项研究的重点是应用ML来预测ESBL和细菌的多药性耐药性 (MDR).
研究的目的:
- 采用ML算法来预测ESBL和MDR细菌的发生.
- 确定与ESBL出现和抵抗相关的关键特征.
- 选择最佳的ML方法来预测ESBL概况.
主要方法:
- 在489个抗生素耐药性测试患者报告上训练了6个ML算法.
- 利用微生物学和临床数据来预测ESBL和MDR概况.
- 基于预测性能的选择最佳的ML方法,用于识别相关特征.
主要成果:
- 大肠杆菌 (E. coli) 是最常见的ESBL产生微生物 (82%),经常与尿路感染 (尿路感染,68.7%) 相关.
- 分类和回归树 (CART) 和随机森林 (RF) 是最有效的ML算法.
- 患者的年龄和抗生素类别,如 cefuroxime,ceftazidime 和 ciprofloxacin 与ESBL 有关,而阿米卡辛和美罗则显示出反向关系.
结论:
- CART和RF ML算法可以准确预测ESBL的关键特征.
- 监测ESBL感染趋势对于有效的抗生素治疗管理至关重要.
- 机械制造为了解和对抗抗菌素耐药性提供了一种有价值的工具.
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