Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs - 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; et al. (Scientific Data)
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.