Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records

• URL: http://arxiv.org/abs/2503.07664v1
• Date: Sat, 08 Mar 2025 21:28:12 GMT
• Title: Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records
• Authors: Fateme Nateghi Haredasht, Fatemeh Amrollahi, Manoj Maddali, Nicholas Marshall, Stephen P. Ma, Lauren N. Cooper, Richard J. Medford, Sanjat Kanjilal, Niaz Banaei, Stanley Deresinski, Mary K. Goldstein, Steven M. Asch, Amy Chang, Jonathan H. Chen,
• Abstract summary: The Antibiotic Resistance Microbiology dataset (ARMD) is a de-identified resource derived from electronic health records (EHR)<n>ARMD encompasses data from adult patients, focusing on microbiological cultures, antibiotic susceptibilities, and associated clinical and demographic features.<n>This paper describes the dataset's acquisition, structure, and utility while detailing its de-identification process.
• Score: 2.7989219381501553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates research into antimicrobial resistance (AMR). ARMD encompasses data from adult patients, 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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