Antibiotic Resistance Microbiology Dataset Mass General Brigham (ARMD-MGB)

@article{PhysioNet-armd-mgb-2.0.0,
  author = {Wei, Ziming and Kanjilal, Sanjat},
  title = {{Antibiotic Resistance Microbiology Dataset Mass General Brigham (ARMD-MGB)}},
  journal = {{PhysioNet}},
  year = {2026},
  month = nov,
  note = {Version 2.0.0},
  doi = {10.13026/1smb-ts67},
  url = {https://doi.org/10.13026/1smb-ts67}
}

@article{pollard_physionet_2026, title = {{PhysioNet} as a global platform for biomedical research}, volume = {1}, issn = {3005-0693}, url = {https://doi.org/10.1038/s44360-026-00096-z}, doi = {10.1038/s44360-026-00096-z}, abstract = {Twenty-five years after its first public repository was established, the PhysioNet resource for biomedical and clinical data has enabled tens of thousands of research papers. Further efforts are ongoing to advance secure data sharing, community engagement and scalable research infrastructure.}, number = {8}, journal = {Nature Health}, author = {Pollard, Tom and Moody, Benjamin E. and Lehman, Li-wei H. and Gow, Brian J. and Fernandes, Chrystinne and Xie, Chen and Johnson, Alistair and Mark, Roger G. and Heldt, Thomas}, month = aug, year = {2026}, pages = {792--795},}

Abstract

The Antibiotic Resistance Microbiology Dataset – MGB (ARMD-MGB) is a de-identified resource derived from electronic health records (EHR) that facilitates research in antimicrobial resistance (AMR). ARMD-MGB encompasses data collected from over 200,000 adult patients over 10 years from hospitals in the Mass General Brigham healthcare system. The focus of the data is on over 350,000 microbiological cultures, with associated antibiotic susceptibilities, and clinical and demographic features of the patients who submitted the samples. Key attributes include organism identity, antibiotic susceptibility results, implied susceptibility phenotypes, laboratory and vital sign summaries, and de-identified clinical metadata.

The ARMD-MGB dataset is designed to complement the ARMD-Stanford dataset. Cohort inclusion and exclusion criteria, feature descriptions, and encoding are similar across sites. These datasets support studies on antimicrobial stewardship, causal inference, and clinical decision-making, and are designed to be reusable and interoperable, promoting collaboration and innovation in combating AMR.

Background

Antimicrobial resistance (AMR) arises from the multiscale interaction between evolutionary forces, microbiology, the built environment, and human behavior, making it one of the great challenges of the 21st century [1-3]. In 2019, AMR was linked to nearly 5 million deaths worldwide, including at least 1.27 million directly due to resistant infections [4]. In the U.S., over 2.8 million AMR infections occur annually, resulting in more than 35,000 deaths [5] and costs of more than $2 billion [6].

Machine learning (ML) is a subfield of artificial intelligence (AI) that has emerged as one potential avenue by which to address this complex phenomenon. ML is primarily concerned with the development of algorithms that are able to build a predictive model using a training data set, with little to no human input [7]. Interest in applying ML to electronic health record (EHR) data to understand AMR has intensified over the past decade, reflecting the exponential increase in biological and medical data availability, massive improvements in computational power, and critical breakthroughs in algorithm development [8].

Prior ML studies with EHR data have led to increased insight into AMR epidemiology [9,10], optimal treatment selection [11], and the building of AI-supported decision support tools [12,13], among many other topics. However, the wider adoption of these algorithms is limited by the use of single-center datasets, which limit generalizability, and the use of relatively few model types, along with sparse clinical metadata. To truly harness the power of ML, there is an important need for broadly generalizable datasets that contain diverse clinical practice patterns, patient populations, and patterns of AMR.

The ARMD-MGB and ARMD-Stanford [14] projects represent a pioneering effort to overcome this fundamental gap through the release of fully de-identified, population-based, multi-center, harmonized EHR datasets that contain rich metadata for hundreds of thousands of patients, and are specifically engineered for the study of AMR. Both ARMD datasets differ from previously publicly available datasets in this space, which focus only on genetic and genomic determinants of resistance [15,16], or lack deep clinical metadata [17], and/or lack sufficient power for individual-level prediction across a broad population [13].

ARMD-MGB uses the same inclusion and exclusion criteria, as well as an identical feature engineering pipeline as ARMD-Stanford, but there are some important differences between the two.

• ARMD-MGB is specific to patients who received care within the Mass General Brigham healthcare system, a network of 12 academic and community hospitals located in the New England area, and contains data from 2015 through 2024. ARMD-Stanford is specific to the Stanford Health Care, located in California, and contains data from 1999 through 2024.
• ARMD-MGB categorizes ward type into inpatient / outpatient / emergency room, with urgent care and day surgery encounters identifiable through ordering_mode in microbiology_cohort.csv, while ARMD-Stanford categorizes ward types into inpatient / ICU / outpatient / emergency room.

Methods

Cohort Selection

• Inclusion: Adult patients (age >= 18) who submitted a urine, blood, or respiratory culture for clinical and surveillance purposes to the clinical microbiology laboratory.
• Exclusion: Microbiology cultures with no previous culture within the last 14 days.

Data de-identification

• Patient ages are binned into categories (defined below in the data dictionary).
• Patient ID, Patient Encounter ID, and Microbiology Culture Order ID are mapped to a random integer.
• All dates are randomly shifted for each patient, encounter, and microbiology culture. They remain internally consistent for a given patient and across datasets.
• Laboratory accession numbers and patient medical record numbers are namespaced by hospital before de-identification, as both are unique only within an individual MGB hospital. Cultures are assigned to encounters by an interval join on the admission–discharge window.

Derivation of susceptibility phenotype labels

The medical record contains microbiological testing results for any specimens sent to the clinical microbiology labs of any hospital within the Massachusetts General Brigham healthcare system, which encompasses 12 hospitals in the New England region. The data includes the body site of collection, the identity of the pathogen, and its susceptibility testing results against standard panels of antibiotics. The dataset also contains the date and location category of specimen collection (both anonymized but internally consistent).

The microbiology data contains the susceptibility phenotypic labels reported by the laboratory based on their internal protocols, collapsed to Susceptible, Intermediate or Resistant. Enzyme detection results (for example mecA, blaZ and carbapenemase) are mapped into the same three categories and are not separately identifiable.

Laboratory and vital sign features

For each culture order, laboratory values are summarized as the 25th percentile, 75th percentile, median, first and last observation over lookback windows of 14, 30 and 180 days before collection. Vital signs are summarized the same way over the 48 hours before collection. Cultures with no qualifying measurement in a window are absent from these tables rather than present with empty values.

Implied Susceptibility

Where susceptibility to an antibiotic was not directly measured, a value is inferred from related antibiotics tested on the same culture using organism-specific clinical rules, for eight organism groups: Acinetobacter, Citrobacter, Enterobacter, Escherichia coli, Klebsiella, Proteus, Pseudomonas aeruginosa and Serratia.

Data Description

File descriptions

Identifying feature descriptions (common to all datasets)

• anon_id
  • Description: De-identified patient ID
  • Type: Integer
  • Encoding: random number
  • Units: N/A
  • Range: 1 - 200,604
• pat_enc_csn_id_coded
  • Description: De-identified patient encounter ID, assigned every time a patient visits the hospital for any reason
  • Type: Integer
  • Encoding: random ID de-identification
  • Units: N/A
  • Range: 1 - 353,779
• order_proc_id_coded
  • Description: De-identified microbiology culture ID, assigned for every unique sample received by the lab
  • Type: Integer
  • Encoding: random ID de-identification
  • Units: N/A
  • Range: 1 - 353,680
• order_time_jittered_utc_shifted
  • Description: Microbiology culture order timestamp (anonymized but internally consistent)
  • Type: DateTime
  • Encoding: N/A
  • Units: UTC
  • Range: Anonymized

microbiology_cohort.csv

Description: Microbiology culture data for the study cohort.

• Empty values indicate field is N/A for that culture (i.e., culture was negative)
• culture_description
  • Description: Body site of culture
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: N/A
• organism
  • Description: Organism name assigned by the clinical microbiology laboratory
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: N/A
• antibiotic
  • Description: Full name of the antibiotic tested in AST panel
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: N/A
• ordering_mode
  • Description: Encounter class at collection.
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: Outpatient / Inpatient / Emergency / Observation / ED Observation / Psych Inpatient / Urgent Care / Day Surgery / Surgery Admit
• was_positive
  • Description: Whether the organism is identified in the culture.
  • Type: Binary
  • Encoding: N/A
  • Units: N/A
  • Range: 0 / 1
• susceptibility
  • Description: Whether the identified organism is susceptible to certain antibiotic
  • Type: Categorical.
  • Encoding: N/A
  • Units: N/A
  • Range: Susceptible / Intermediate / Resistant

adi_score.csv

Description: Area Deprivation Index (ADI) scores for the patient based on the ZIP code [20, 21].

• Empty values indicate field is missing
• adi_score
  • Description: Actual ADI score
  • Type: Numerical
  • Encoding: N/A
  • Units: N/A
  • Range: 1.32 - 78.7
• adi_state_rank
  • Description: Ranking of the ADI score within the state
  • Type: Integer
  • Encoding: N/A
  • Units: N/A
  • Range: 1–10

comorbidity.csv

Description: Patient comorbidities

• Empty values indicate field is missing
• ICD10
  • Description: ICD-10 code of diagnosis
  • Type: String
  • Encoding: N/A
  • Units: N/A
  • Range: N/A
• category
  • Description: Comorbidity category of diagnosis. Elixhauser [22] and AHRQ CCSR categorizations are both included, so a single diagnosis may appear under one category from each vocabulary.
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: N/A

demographics.csv

Description: Patient demographics

• Empty values indicate field is missing
• age
  • Description: Age category of patient at time of microbiology culture collection
  • Type: Categorical
  • Encoding: N/A
  • Units: years
  • Range: 18-24 / 25-34 / 35-44 / 45-54 / 55-64 / 65-74 / 75-84 / 85-89 / ≥90
• gender
  • Description: Gender identity of the patient at the time of microbiology culture collection
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: Male / Female / Other

nursing_home_visits.csv

Description: Nursing home visits relative to culture time

• Empty values indicate field is missing
• nursing_home_visit_culture
  • Description: Time interval between the last nursing home visit and the current microbiology culture
  • Type: Numerical
  • Encoding: N/A
  • Units: days
  • Range: 0 - 154
• visit_date_shifted
  • Description: Date of last nursing home visit relative to culture (anonymized but internally consistent)
  • Type: DateTime
  • Encoding: N/A
  • Units: UTC
  • Range: Anonymized

prior_abx.csv

Description: Prior exposures to antimicrobials relative to the current microbiology culture

• Empty values indicate field is missing
• medication_time_to_cultureTime
  • Description: Time interval between the antibiotic order and the current microbiology culture
  • Type: Numerical
  • Encoding: N/A
  • Units: days
  • Range: 0 - 3,403
• medication_name
  • Description: Full name of the antibiotic taken or ordered
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: N/A
• medication_category
  • Description: Antibiotic class (anti-staph beta-lactam, fluoroquinolone, etc.)
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: N/A
• medicationOrderDTS_shifted
  • Description: Timestamp of prior antibiotic (anonymized but internally consistent)
  • Type: DateTime
  • Encoding: N/A
  • Units: UTC
  • Range: Anonymized

prior_microbial_resistance.csv

Description: Prior antibiotic resistance history for the patient relative to the current microbiology culture. Only prior results reported as Resistant are included; susceptible and intermediate results are not present in this file.

• Empty values indicate the patient had no prior resistant result before the current culture
• organism
  • Description: Name of the organism identified in a prior culture with a resistant result
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: N/A
• antibiotic
  • Description: Full name of the antibiotic to which the prior organism was resistant
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: N/A
• resistant_time_to_cultureTime
  • Description: Time interval between collection of the prior resistant culture and the current microbiology culture
  • Type: Numerical
  • Encoding: N/A
  • Units: days
  • Range: 1 - 3,325
• recorded_resistsant_time_shifted
  • Description: Timestamp at which the prior resistant result was reported (anonymized but internally consistent). Note this records the result time, whereas resistant_time_to_cultureTime is measured from the collection time, so the two do not reconcile. Field name spelling matches the source data header.
  • Type: DateTime
  • Encoding: N/A
  • Units: UTC
  • Range: Anonymized

prior_org.csv

Description: Prior organism history for patient relative to current microbiology culture

• Empty values indicate field is missing
• prior_infecting_organism_days_to_culture
  • Description: Time interval between prior organism and current microbiology culture
  • Type: Numerical
  • Encoding: N/A
  • Units: days
  • Range: 1 - 3,326
• prior_organism
  • Description: Organism category for the organism in prior microbiology culture (categorization schema shown below)
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: N/A
• prior_infecting_organism_recorded_time_shifted
  • Description: Recorded time of prior microbiology culture (anonymized but internally consistent)
  • Type: DateTime
  • Encoding: N/A
  • Units: UTC
  • Range: Anonymized

prior_procedures.csv

Description: Prior procedures relative to current microbiology culture. Each procedure has a unique timestamp (not included). Multiple rows with the same procedure indicate a duration. For example, the following snippet indicates the patient was mechanically ventilated for 3 days, 62 to 64 days prior to a culture ordered on 2075-01-19:

• Empty values indicate field is missing
• procedure_description
  • Description: Type of procedure (surgical, central line, dialysis, ventilation, etc.), inferred from CPT codes
  • Type: Categorical
  • Encoding: mechvent (mechanical ventilation); surgical_procedure (of any type); cvc (central venous catheter); dialysis; urethral_catheter
  • Units: N/A
  • Range: N/A
• procedure_days_culture
  • Description: Time interval between procedure and current microbiology culture
  • Type: Numerical
  • Encoding: N/A
  • Units: days
  • Range: 1 - 4,663

ward_type.csv

Description: Setting for the clinical encounter when the microbiology culture was taken.

• Empty values indicate field is missing
• hosp_ward_IP
  • Description: Flag for inpatient encounter
  • Type: Binary
  • Encoding: 1: yes / 0: no
  • Units: N/A
  • Range: 0 / 1
• hosp_ward_OP
  • Description: Flag for outpatient encounter
  • Type: Binary
  • Encoding: 1: yes / 0: no
  • Units: N/A
  • Range: 0 / 1
• hosp_ward_ER
  • Description: Flag for emergency room encounter
  • Type: Binary
  • Encoding: 1: yes / 0: no
  • Units: N/A
  • Range: 0 / 1
• hosp_ward_ICU
  • Description: Flag for ICU encounter, currently all NA, placeholder for synchronization.
  • Type: Binary
  • Encoding: 1: yes / 0: no
  • Units: N/A
  • Range: 0 / 1

implied_susceptibility.csv

Description: Microbiology culture data for the study cohort, augmented with susceptibility results inferred from organism-specific clinical inference rules where susceptibility was not directly measured. Inference covers eight organism groups: Acinetobacter, Citrobacter, Enterobacter, Escherichia coli, Klebsiella, Proteus, Pseudomonas aeruginosa, Serratia.

• Empty values indicate field is missing. implied_susceptibility is empty where susceptibility was measured directly.
• ordering_mode
  • Description: Class of the clinical encounter at the time of culture collection
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: Inpatient / Outpatient / Emergency / Observation / ED Observation / Psych Inpatient / Urgent Care / Day Surgery / Surgery Admit
• culture_description
  • Description: Body site of culture
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: URINE / BLOOD / RESPIRATORY_TRACT
• was_positive
  • Description: Flag if an organism was identified in the culture
  • Type: Binary
  • Encoding: 1: yes / 0: no
  • Units: N/A
  • Range: 0 / 1
• organism
  • Description: Organism name assigned by the clinical microbiology laboratory
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: N/A
• antibiotic
  • Description: Full name of the antibiotic tested
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: N/A
• susceptibility
  • Description: Susceptibility result reported by the laboratory
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: Susceptible / Intermediate / Resistant
• implied_susceptibility
  • Description: Susceptibility inferred from related antibiotics tested on the same culture and organism, where a laboratory result was not reported
  • Type: Categorical
  • Encoding: N/A
  • Units: N/A
  • Range: Susceptible / Resistant

labs.csv

Description: Laboratory results for the patient summarised over fixed lookback windows before the microbiology culture order. One row per culture per lookback window.

• Empty values indicate no qualifying result for that laboratory test within the window. Cultures with no laboratory results in any window are absent from the file.
• Period_Day
  • Description: Length of the lookback window before the culture order over which the summary statistics were computed
  • Type: Integer
  • Encoding: N/A
  • Units: days
  • Range: 14 / 30 / 180
• Q25_<lab>
  • Description: 25th percentile of the values recorded for <lab> within the window
  • Type: Numerical
  • Encoding: N/A
  • Units: see table below
  • Range: see table below
• Q75_<lab>
  • Description: 75th percentile of the values recorded for <lab> within the window
  • Type: Numerical
  • Encoding: N/A
  • Units: see table below
  • Range: see table below
• median_<lab>
  • Description: Median of the values recorded for <lab> within the window
  • Type: Numerical
  • Encoding: N/A
  • Units: see table below
  • Range: see table below
• first_<lab>
  • Description: Earliest value recorded for <lab> within the window
  • Type: Numerical
  • Encoding: N/A
  • Units: see table below
  • Range: see table below
• last_<lab>
  • Description: Latest value recorded for <lab> within the window
  • Type: Numerical
  • Encoding: N/A
  • Units: see table below
  • Range: see table below

<lab> is one of the following thirteen measurements. Ranges are observed across all five summary statistics. No upper physiologic bound was applied, so extreme values are present.

vitals.csv

Description: Vital signs for the patient summarised over the 48 hours before the microbiology culture order. One row per culture.

• Empty values indicate no qualifying measurement for that vital sign within the window. Cultures with no vital sign measurements are absent from the file.
• Q25_<vital>
  • Description: 25th percentile of the values recorded for <vital> within the window
  • Type: Numerical
  • Encoding: N/A
  • Units: see table below
  • Range: see table below
• Q75_<vital>
  • Description: 75th percentile of the values recorded for <vital> within the window
  • Type: Numerical
  • Encoding: N/A
  • Units: see table below
  • Range: see table below
• median_<vital>
  • Description: Median of the values recorded for <vital> within the window
  • Type: Numerical
  • Encoding: N/A
  • Units: see table below
  • Range: see table below
• first_<vital>
  • Description: Earliest value recorded for <vital> within the window
  • Type: Numerical
  • Encoding: N/A
  • Units: see table below
  • Range: see table below
• last_<vital>
  • Description: Latest value recorded for <vital> within the window
  • Type: Numerical
  • Encoding: N/A
  • Units: see table below
  • Range: see table below

<vital> is one of the following five measurements. Ranges are observed across all five summary statistics. A lower bound was applied at extraction, as shown; no upper bound was applied except for respiratory rate, so extreme values are present.

Definition of organism categories:

Drug codes used for the categorization:

• CAZ - Ceftazidime
• CRO - Ceftriaxone
• FEP - Cefepime
• FOX - Cefoxitin
• OXA - Oxacillin
• SAM - Ampicillin-sulbactam
• SXT - Trimethoprim-sulfamethoxazole
• TZP - Piperacillin-tazobactam
• VAN - Vancomycin

Usage Notes

Getting Started

The ARMD-MGB dataset is provided as a collection of de-identified CSV files. Each table can be linked using the anon_id (de-identified patient identifier) and, where applicable, the pat_enc_csn_id_coded (encounter identifier) or order_proc_id_coded (microbiology accession identifier).

Below is a minimal example demonstrating how to load and interpret key variables:

Load microbiology culture data

import pandas as pd

micro = pd.read_csv("microbiology_cohort.csv")
print(micro.head())

Interpret antibiotic susceptibility phenotype

phenotype_counts = micro["susceptibility"].value_counts()
print(phenotype_counts)

Intended Uses

ARMD-MGB is intended to support research on:

• Antimicrobial resistance (AMR) epidemiology, including longitudinal and organism-specific resistance trends.
• Antibiotic stewardship and prescribing practices, by linking prior antimicrobial exposure to subsequent resistance outcomes.
• Causal inference and predictive modeling, through integration of microbiological, clinical, and environmental covariates.
• Cross-site and temporal benchmarking, when used in conjunction with ARMD-Stanford or other harmonized datasets.

Because ARMD-MGB adheres to a harmonized schema shared with ARMD-Stanford, analyses can be conducted across both datasets to assess reproducibility and generalizability across health systems.

Limitations

• Geographic and institutional context: Data originate from the Mass General Brigham (MGB) healthcare system in New England; therefore, resistance patterns may differ from other regions.
• Temporal heterogeneity: Changes in clinical practice and diagnostic platforms over the 10-year period may influence detection rates.

Release Notes

Version 2.0.0

This is a major revision that aligns ARMD-MGB with the ARMD-Stanford schema. File names and column names have changed throughout, and code written against version 1.0.0 will not run unmodified against version 2.0.0.

Corrected culture-to-patient identifier assignment. In version 1.0.0, we observed some mismatch between order_proc_id_coded and anon_id, mainly due to asynchronized deidentification process. Now the mismatch is corrected.

After the correction, across all thirteen tables, no encounter identifier and no culture identifier maps to more than one patient identifier, and every identifier present in a feature table is present in the cohort. A residual 3,461 of 353,603 culture identifiers (1.0%) map to two encounters belonging to the same patient, arising from overlapping encounter windows; patient-level linkage for these records is unambiguous.

New tables. labs.csv and vitals.csv add laboratory and vital sign summaries for 62.1% and 59.6% of cohort cultures respectively. implied_susceptibility.csv adds susceptibility inferred from organism-specific clinical rules for eight organism groups. microbial_resistance.csv provides prior resistant results relative to each culture.

Schema changes. Susceptibility is now a single three-valued column derived from the laboratory-reported phenotype. The granular susceptibility testing fields of version 1.0.0: test method, minimum inhibitory concentrations and disk diameters, beta-lactamase enzyme results and CLSI M-100 (2022) interpretations, are not part of the harmonized schema, as is prior_micro_deid_tj.csv. Urgent care and day surgery encounters are identifiable through ordering_mode.

Cohort. The corrected pipeline yields 200,543 patients, 353,628 encounters and 353,603 culture orders, compared with 226,659 / 497,096 / 970,165 in version 1.0.0. The reduction follows from the corrected encounter assignment and exclusion rule, which no longer duplicate cultures across encounters and patients.

Version 1.0.0:

Initial public release of the dataset.

Ethics

This study was approved by the Institutional Review Board (IRB) of Massachusetts General Brigham healthcare, protocol 2017P000682.

Conflicts of Interest

The authors declare no conflicts of interest

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Cited by

2 publications cite this project. The most recent are listed below.

• Progressing From Association to Causation: Examining Prior Antibiotic Exposure and Antimicrobial Resistance Across Three US Health Systems Using Causal Machine Learning
  Aravind V. Kuruvikkattil, Shikhar Shukla, Leo Anthony Celi et al. Clinical Infectious Diseases, 2026.
• How ready are we to use artificial intelligence in our fight against antimicrobial resistance? An ESGAID and EAAS perspective
  Daniele Roberto Giacobbe, Rafi Ahmad, Fatih Mehmet Akıllı et al. Expert Review of Anti-infective Therapy, 2026.

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Citation data from OpenAlex. Papers that use the project without citing it may be missing.