Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs
This provides a reusable dataset for researchers and clinicians combating antimicrobial resistance, but it is incremental as it primarily offers a new resource rather than novel methods.
The authors tackled the problem of limited resources for antimicrobial resistance research by creating the Antibiotic Resistance Microbiology Dataset (ARMD), a de-identified dataset derived from electronic health records over 15 years at two hospitals, which includes microbiological cultures, antibiotic susceptibilities for 55 antibiotics, and clinical features to support studies on antimicrobial stewardship and clinical decision-making.
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.