Evidence map›Paper›PMID 40715119›Full record

ArticleScientific data2025

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 and 6 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Cultryx: Precision Diagnostic Stewardship for Blood Cultures Using Machine Learning.medRxiv : the preprint server for health sciences · 2026
    Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Fateme Nateghi HaredashtStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA. fnateghi@stanford.edu.ORCID 0000-0002-8874-8835
Fatemeh AmrollahiStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Manoj V MaddaliDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Nicholas MarshallDivision of Pediatric Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA.ORCID 0009-0003-6051-5890
Stephen P MaDivision of Hospital Medicine, Stanford University, Stanford, CA, USA.ORCID 0000-0003-3738-9569
Lauren N CooperClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Andrew O JohnsonInformation Services, East Carolina University, Greenville, NC, USA.
Ziming WeiDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts, USA.
Richard J MedfordClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Sanjat KanjilalDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts, USA.ORCID 0000-0002-1221-5725
Niaz BanaeiDivision of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Stanley DeresinskiDivision of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Mary K GoldsteinDepartment of Health Policy, Stanford University School of Medicine, Stanford, CA, USA.
Steven M AschDivision of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
Amy ChangDivision of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Jonathan H ChenStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.

Funding

Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant BacteriaR01AI178121 · NIAID · STANFORD UNIVERSITY · PI JONATHAN H. CHEN · 2023 to 2026
$3.1M
NIAID NIH HHS R01 AI178121U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) R01AI179155
6 · The paper itself

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.

Indexed as

Drug Resistance, BacterialDrug Resistance, MicrobialElectronic Health RecordsAnti-Bacterial AgentsAntimicrobial StewardshipBig DataHumansAnti-Bacterial Agents

Identifiers

PMID40715119
PMCPMC12297523

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.