抗生素耐药性微生物学数据集 (ARMD):来自EHRs的抗生素耐药性的资源
Fateme Nateghi Haredasht1, Fatemeh Amrollahi2, Manoj V Maddali3
1Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA. fnateghi@stanford.edu.
Scientific data
|July 27, 2025
概括
抗生素耐药性微生物学数据集 (ARMD) 为抗生素耐药性 (AMR) 研究提供了有价值的大数据. 这种非识别资源支持研究抗生素管理和临床决策,以打击AMR.
科学领域:
- 微生物学 微生物学
- 医疗信息学 医疗信息学
- 流行病学 流行病学
背景情况:
- 抗菌素耐药性 (AMR) 构成了全球健康的重大威胁.
- 有效的研究需要全面的,非识别的患者数据.
- 现有的数据集可能缺乏先进抗菌耐药性研究所需的范围或细节.
研究的目的:
- 引入抗生素耐药性微生物学数据集 (ARMD).
- 描述ARMD的收购,结构和解除身份的过程.
- 突出ARMD对于抗菌药物耐药性研究和抗菌药物管理的有用性.
主要方法:
- 从电子健康记录 (EHR) 在两个学术附属医院收集了超过15年的数据.
- 数据集包括微生物培养,55种抗生素的抗生素敏感性测试结果,以及临床/人口统计数据.
- 为了保护患者的隐私,实施了严格的非识别程序.
主要成果:
- ARMD包含来自成年患者的大数据,包括生物体识别和易感性模式.
- 该数据集具有暗示的敏感性规则和非识别的患者信息.
- 该结构支持合作研究的可重复使用性和互操作性.
结论:
- ARMD是一个有价值的,非识别的资源,用于推进抗菌药物耐药性研究.
- 该数据集促进了对抗微生物药物管理,因果推断和临床决策的研究.
- 在抗微生物药物耐药性防治方面,ARMD促进合作和创新.
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