Related Experiment Video
Updated: Sep 13, 2025

Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes
Published on: March 3, 2023
Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs
Fateme Nateghi Haredasht1, Fatemeh Amrollahi2, Manoj V Maddali3
1Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA. fnateghi@stanford.edu.
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.
More Related Videos
Related Concept Videos
Development of Antibiotic Resistance
Antimicrobial Effectiveness
Antibiotic Selection
Urine Studies II: Urine Culture and Sensitivity Test

