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<p>The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates research in antimicrobial resistance (AMR). ARMD encompasses big data from adult patients collected from over 15 years at two academic-affiliated hospitals, focusing on microbiological cultures, antibiotic susceptibilities, and associated clinical and demographic features. Key attributes include organism identification, susceptibility patterns for 55 antibiotics, implied susceptibility rules, and de-identified patient information. This dataset supports studies on antimicrobial stewardship, causal inference, and clinical decision-making. ARMD is designed to be reusable and interoperable, promoting collaboration and innovation in combating AMR. This paper describes the dataset’s acquisition, structure, and utility while detailing its de-identification process.</p>

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Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月26日

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  • Fateme Nateghi Haredasht,
  • Fatemeh Amrollahi,
  • Manoj V. Maddali,
  • Nicholas Marshall,
  • Stephen P. Ma,
  • Lauren N. Cooper,
  • Andrew O. Johnson,
  • Ziming Wei,
  • Richard J. Medford,
  • Sanjat Kanjilal,
  • Niaz Banaei,
  • Stanley Deresinski,
  • Mary K. Goldstein,
  • Steven M. Asch,
  • Amy Chang,
  • Jonathan H. Chen

摘要

The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates research in antimicrobial resistance (AMR). ARMD encompasses big data from adult patients collected from over 15 years at two academic-affiliated hospitals, focusing on microbiological cultures, antibiotic susceptibilities, and associated clinical and demographic features. Key attributes include organism identification, susceptibility patterns for 55 antibiotics, implied susceptibility rules, and de-identified patient information. This dataset supports studies on antimicrobial stewardship, causal inference, and clinical decision-making. ARMD is designed to be reusable and interoperable, promoting collaboration and innovation in combating AMR. This paper describes the dataset’s acquisition, structure, and utility while detailing its de-identification process.