Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs
March 08, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Fateme Nateghi Haredasht, Fatemeh Amrollahi, Manoj 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
arXiv ID
2503.07664
Category
q-bio.QM
Cross-listed
cs.IR,
cs.LG,
stat.AP
Citations
0
Venue
arXiv.org
Last Checked
3 months ago
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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