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 Amrollahi1, Manoj V Maddali2
1Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
ArXiv
|July 31, 2025
概括
抗生素耐药性微生物学数据集 (ARMD) 提供了来自电子健康记录的大量非识别资源,以推进抗生素耐药性 (AMR) 研究. 这些大数据支持对抗生素管理和对抗AMR的临床决策的研究.
科学领域:
- 微生物学 微生物学
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
背景情况:
- 抗菌素耐药性 (AMR) 是一个日益增长的全球健康威胁.
- 有效的研究需要全面的,可访问的数据集.
- 电子健康记录 (EHR) 包含有价值的微生物学和临床数据.
研究的目的:
- 引入抗生素耐药性微生物学数据集 (ARMD).
- 描述ARMD的结构,收购和解除身份的过程.
- 突出数据集对抗药性耐药性研究的有用性.
主要方法:
- 来自两个医院15年来成年患者的电子病历数据.
- 包括微生物培养物,抗生素敏感性测试 (AST) 结果和临床/人口统计数据.
- 严格的非识别过程适用于患者信息.
主要成果:
- 该ARMD包含55种抗生素的细节性生物体识别和敏感性模式.
- 包括隐含的敏感性规则和相关的非识别患者特征.
- 数据集的结构是为了可重复使用性和互操作性.
结论:
- ARMD是一个有价值的,非识别的大数据资源,用于AMR研究.
- 促进对抗微生物药物管理,因果推断和临床决策方面的研究.
- 促进合作,打击抗微生物药物耐药性的威胁.
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