Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs
Fateme Nateghi Haredasht, Fatemeh Amrollahi, Manoj V. Maddali, N. J. Marshall, P. Stephen, Lauren N Cooper, Andrew O. Johnson, Ziming Wei, Richard J Medford, Sanjat Kanjilal, Niaz Banaei, Stanley C. Deresinski, Mary K. Goldstein, Steven M. Asch, Amy Chang, Jonathan H. Chen
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.