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

Fateme Nateghi Haredasht, Fatemeh Amrollahi, Manoj Maddali, Nicholas Marshall, Stephen P. Ma +11 more
3/8/2025
q-bio.QMcs.IRcs.LG

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Abstract

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.

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Cite this paper

@article{haredasht2025antibiotic,
  title  = {Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs},
  author = {Fateme Nateghi Haredasht and Fatemeh Amrollahi and Manoj Maddali and Nicholas Marshall and Stephen P. Ma and Lauren N. Cooper and Andrew O. Johnson and Ziming Wei and Richard J. Medford and Sanjat Kanjilal and Niaz Banaei and Stanley Deresinski and Mary K. Goldstein and Steven M. Asch and Amy Chang and Jonathan H. Chen},
  year   = {2025},
  eprint = {2503.07664},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.07664v2}
}

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