利用大数据,统计和机器学习来预测耐药性E. coli的出现. 大肠杆菌感染 感染
Rim Hur1,2,3, Stephine Golik1,4, Yifan She1,3
1Department of Inpatient Pharmacy, Kaiser Permanente, One Kaiser Plaza, Oakland, CA 94612, USA.
Pharmacy (Basel, Switzerland)
|March 25, 2024
概括
减少塞法林的使用可以降低耐药性大肠杆菌感染,这是对抗微生物药物管理的关键发现. 这项研究模拟了抗生素耐药性趋势,以预测和管理医疗保健成本.
科学领域:
- 传染性疾病 传染性疾病
- 计算生物学 计算生物学
- 卫生经济学 卫生经济学
背景情况:
- 耐药的格拉姆阴性细菌感染显著增加了住院时间和费用.
- 大肠杆菌 (大肠杆菌) 是一种常见的病原体,抗生素耐药性构成越来越大的威胁.
- 了解抗生素使用和耐药性之间的关系对于有效的抗菌药物管理至关重要.
研究的目的:
- 通过使用统计和机器学习模型,探索抗生素使用与抗生素耐药性之间的关系.
- 预测与耐药大肠杆菌感染相关的临床和财务成本.
- 为抗微生物管理计划 (ASP) 提供一个框架,以使用数据驱动的干预措施.
主要方法:
- 从凯泽永久医疗机构获得关于抗生素利用和微生物培养耐药性/敏感性的数据 (2013年4月 - 2019年12月).
- 采用时间序列算法,包括自回归集成移动平均 (ARIMA),神经网络和随机森林来建模抗生素耐药性趋势.
- 使用平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 评估模型性能,最佳模型预测了2020年的阻力率.
主要成果:
- 在预测抗生素耐药性趋势方面,ARIMA模型表现最好,特别是在Cefazolin和Cephalexin方面.
- 减少塞法林的使用被认为是降低耐药大肠杆菌感染率的潜在策略.
- 尽管piperacillin/tazobactam不是模型中表现最好的药物,但由于其广泛的范围,它显示出作为ASP的干预目标的潜力.
结论:
- 统计和机器学习模型可以有效地预测抗生素耐药性趋势,并为抗菌药物管理干预提供信息.
- 特定区域的数据对于在ASP内定制干预措施是有价值的.
- 这项研究为采用先进的分析方法来打击抗生素耐药性和管理相关成本提供了一个框架.
关键词:
在阿里马,阿里马就是阿里马.抗生素耐药性 抗生素耐药性抗微生物耐药性 抗微生物耐药性抗微生物药物管理计划 (ASP)逗留时间 (LOS)神经网络的神经网络的神经网络随机森林算法 随机森林算法时间序列模型更多相关视频
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