Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs
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| Format: | Preprint |
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2025
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| author | Haredasht, Fateme Nateghi Amrollahi, Fatemeh Maddali, Manoj Marshall, Nicholas Ma, Stephen P. Cooper, Lauren N. Johnson, Andrew O. Wei, Ziming Medford, Richard J. Kanjilal, Sanjat Banaei, Niaz Deresinski, Stanley Goldstein, Mary K. Asch, Steven M. Chang, Amy Chen, Jonathan H. |
| author_facet | Haredasht, Fateme Nateghi Amrollahi, Fatemeh Maddali, Manoj Marshall, Nicholas Ma, Stephen P. Cooper, Lauren N. Johnson, Andrew O. Wei, Ziming Medford, Richard J. Kanjilal, Sanjat Banaei, Niaz Deresinski, Stanley Goldstein, Mary K. Asch, Steven M. Chang, Amy Chen, Jonathan H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07664 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs Haredasht, Fateme Nateghi Amrollahi, Fatemeh Maddali, Manoj Marshall, Nicholas Ma, Stephen P. Cooper, Lauren N. Johnson, Andrew O. Wei, Ziming Medford, Richard J. Kanjilal, Sanjat Banaei, Niaz Deresinski, Stanley Goldstein, Mary K. Asch, Steven M. Chang, Amy Chen, Jonathan H. Quantitative Methods Information Retrieval Machine Learning Applications 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. |
| title | Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs |
| topic | Quantitative Methods Information Retrieval Machine Learning Applications |
| url | https://arxiv.org/abs/2503.07664 |