# CHORUS Dataset Documentation

Datasheet for Dataset - Human Readable Format

🎯

## Motivation

Why was the dataset created?

Purposes

Description | ID | Name
--- | --- | ---
Answer the grand challenge of improving recovery from acute illness by developing high-resolution multi-center datasets as a critical first step towards actionable and trustworthy AI in critical care. Address the urgent need for infrastructure to support artificial intelligence and machine learning (AI/ML) in critical care settings. | purpose-001 | Improve recovery from acute illness
Develop a publicly available, AI-ready critical care dataset from more than 100,000 critically ill patients while ensuring methods promote privacy, accountability, and clinical benefit. Generate the most diverse, high-resolution, ethically sourced dataset for AI/ML applications in acute and critical care. | purpose-002 | Create AI-ready critical care dataset
Unify standards to harmonize multi-modal EHR, waveform, imaging, and text data. Develop software and tooling to interact with and extract insight from clinical data in diverse formats. Create validated semantic mappings for connecting clinical data in various source formats to international standards (OMOP Common Data Model). | purpose-003 | Establish data standards and tools
Ensure comprehensive sets of patient conditions and clinical treatment strategies with appropriate contextual factors such as geographic distance to nearest hospital and Social Determinants of Health. Develop the skills and workforce for a next generation of diverse academic and community AI scientists through comprehensive training and education programs. | purpose-004 | Promote diversity and health equity

Funders

- ID
    funder-001
  Name
    NIH Common Fund Bridge2AI Program
  Description
    Funded through National Institutes of Health grant 1OT2OD032701-01, administered by NIH Office of the Director. Opportunity Number: OTA-21-008. Study Section: Data Coordination, Mapping, and Modeling [DCMM]. Fiscal Year 2022. Total funding in 2022: $5,880,300 (all direct costs). Project dates: September 1, 2022 to November 30, 2026 (with no-cost extension approved).

📊

## Composition

What do the instances represent?

Instances

1. ID
     instance-001
   Name
     Critically ill patients
   Description
     Individual critically ill patients requiring acute or critical care admitted to intensive care units or similar hospital settings across 14 data acquisition centers. As of November 2024, dataset covers 14 different hospitals with 23,400 unique admissions. Target enrollment exceeds 100,000 critically ill patients. Retrospective data collection from patients with acute or critical illness.
   Instance Type
     Human subjects - critically ill patients admitted to participating hospitals between data collection timeframe (specific dates vary by site, ongoing collection through November 2026).

Subpopulations

Description | ID | Name
--- | --- | ---
Patients distributed across 14 different hospitals within the CHoRUS network, ensuring geographic and institutional diversity in critical care settings. | subpop-001 | Critically ill patients by hospital
Subset of patients with complete data across multiple modalities: structured EHR, waveform telemetry, imaging, clinical notes, and EEG (availability varies). | subpop-002 | Patients with complete multi-modal data

Distribution Formats

Access Urls | Description | ID | Name
--- | --- | --- | ---
https://chorus4ai.org/ | Structured electronic health record data distributed in OMOP Common Data Model format. Enables use of OHDSI tool stack for analysis. Available in secure enclave with controlled access. | format-001 | OMOP Common Data Model
https://chorus4ai.org/ | Waveform telemetry data distributed in WFDB (WaveForm DataBase) format following extended PhysioNet schema. Available in secure enclave with controlled access. | format-002 | WFDB waveform format
https://chorus4ai.org/ | Medical imaging data distributed in DICOM format with comprehensive metadata. De-identification in process, planned for controlled access in secure enclave. | format-003 | DICOM imaging format
https://chorus4ai.org/ | Clinical notes distributed as OHNLP-tokenized text following open source schema. Stored locally at sites except tokens, planned for controlled access. | format-004 | OHNLP tokenized text
https://chorus4ai.org/ | EEG waveform data distributed in EDF+ (European Data Format) and Persyst formats following open source schemas. Extraction in process, planned for controlled access. | format-005 | EDF+ and Persyst EEG formats

🔍

## Collection Process

How was the data acquired?

ID
https://chorus4ai.org/
Name
CHoRUS
Title
Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI
Description
CHoRUS for Equitable AI is a Bridge2AI data generation project developing the most diverse, high-resolution, ethically sourced, AI-ready critical care dataset to answer the grand challenge of improving recovery from acute illness. The project spans 20 academic centers (14 data acquisition centers) and creates a publicly available dataset of over 100,000 critically ill patients with multi-modal data including structured EHR, waveform telemetry, medical imaging, EEG, and clinical notes. All data is standardized to the OMOP Common Data Model with additional formats (DICOM, WFDB, OHNLP tokenization) and includes comprehensive metadata schemas. Patient-focused efforts determine ethical and legal approaches to manage privacy and bias while accounting for Social Determinants of Health. A visualization and annotation environment labels data with targets important for prediction. The project emphasizes skills and workforce development for a next generation of diverse academic and community AI scientists through training programs and partnerships with AIM-AHEAD. As of November 2024, the dataset covers 14 different hospitals with 23,400 unique admissions.
Page
https://chorus4ai.org/
Language
en
Keywords

- CHoRUS
- Bridge2AI
- critical care
- acute illness
- AI-ready dataset
- OMOP Common Data Model
- electronic health records
- EHR
- waveform telemetry
- medical imaging
- DICOM
- EEG
- clinical notes
- OHNLP
- health equity
- social determinants of health
- federated access
- multi-modal data
- high-resolution data
- ethical AI
- trustworthy AI
- workforce development
- data standardization
- privacy preservation
- bias mitigation

Research Gaps Addressed

Description | ID | Name
--- | --- | ---
Address the absence of large-scale, diverse, high-resolution multi-center datasets for critical care AI/ML by creating a dataset spanning 20 academic centers with 100,000+ critically ill patients and ensuring balanced, diverse cohorts through federated access and sampling methods. | gap-001 | Lack of diverse multi-center critical care datasets
Overcome the lack of unified standards in critical care data by harmonizing multi-modal EHR, waveform, imaging, and text data to OMOP Common Data Model and other international standards (DICOM, WFDB, OHNLP). | gap-002 | Insufficient data standardization
Address ethical and legal challenges in AI through patient-focused efforts that determine approaches to manage privacy and bias while accounting for Social Determinants of Health and performing community-facing ethics focus groups. | gap-003 | Privacy and bias concerns in AI
Bridge the gap in diverse AI/ML workforce through comprehensive educational approaches, training programs (including AIM-AHEAD partnership), and cultivation of expertise in lay and scientific communities to improve AI literacy and utilization. | gap-004 | Limited AI workforce diversity

Creators

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
Contributor | Eric S. Rosenthal | creator-001 | -
Contributor | Azra Bihorac | creator-002 | -
Contributor | Ashley Cordes | creator-003 | -
Contributor | Gilles Clermont | creator-004 | -
Contributor | Gari David Clifford | creator-005 | -
Contributor | Barbara J. Evans | creator-006 | -
Contributor | Xiao Hu | creator-007 | -
Contributor | Rishikesan Kamaleswaran | creator-008 | -
Contributor | Yulia A. Levites Strekalova | creator-009 | -
Contributor | Parisa Rashidi | creator-010 | -
Contributor | Cynthia Rudin | creator-011 | -
Contributor | Ishan Canty Williams | creator-012 | -
Contributor | Andrew Ewing Williams | creator-013 | -
Contributor | Xiaoqian Jiang | creator-014 | -
Contributor | Morteza Zabihi | creator-015 | -
Contributor | Zhenhong Hu | creator-016 | -
Contributor | Debora Simmons | creator-017 | -
Contributor | Andrew Williams | creator-018 | -
Contributor | Aliyah Geer | creator-019 | -

Subsets

Description | ID | Name
--- | --- | ---
Primary dataset with controlled access requiring data use agreement and licensing. Includes OMOP-standardized structured EHR data, waveform telemetry, and associated metadata. As of November 2024, OMOP and telemetry data available in secure enclave. Registration required with institution email, and all participants must sign licensing agreement. | subset-001 | Controlled Access Dataset
Clinical notes extracted and tokenized using OHNLP (Open Health Natural Language Processing) toolkit. Stored locally at contributing sites (except tokens) with planned controlled access. Uses OHNLP open source schema for standardization. | subset-002 | Clinical Notes (Tokenized)
Imaging data from PACS (Picture Archiving and Communication System) in DICOM format. De-identification in process as of November 2024, planned for controlled access. DICOM schema metadata available. | subset-003 | Medical Imaging
Electroencephalography (EEG) waveform data from hospital databases. Stored in EDF+ (European Data Format) and Persyst formats. Extraction in process as of November 2024, planned for controlled access. Open source EDF+ and Persyst schema available. | subset-004 | EEG Waveforms
Subsets of the dataset currently being used for training activities and publications as the dataset undergoes expansion and quality assurance processes. | subset-005 | Training and Publication Datasets

Sampling Strategies

1. ID
     sampling-001
   Name
     Multi-center federated sampling
   Description
     Federated access enables sampling methods to ensure a balanced and diverse cohort across 20 academic centers (14 data acquisition centers). Legal framework established for collecting data at scale with sampling to ensure comprehensive sets of patient conditions and clinical treatment strategies.
   Sample
     True
   Random Sampling
     False
   Representative Sample
     True
   Strategies
     - Federated multi-center data collection across 14 hospitals
     - Sampling for balanced and diverse patient populations
     - Inclusion of contextual factors (geographic distance to hospital, Social Determinants of Health)
     - Community-facing ethics focus groups to determine appropriate data for public sharing
     - Legal framework for data collection at scale

Collection Mechanisms

Description | ID | Name
--- | --- | ---
Retrospective data collection from electronic health record systems at 14 data acquisition centers. Data extracted includes demographics, medication administration (dosing time-stamped upon each infusion change or dose administration), procedures, nursing flowsheets (high-frequency documentation), diagnoses, and clinical notes. | collection-001 | Retrospective EHR extraction
Continuous waveform telemetry data captured from bedside monitors through gateway/middleware systems. Stored in WFDB (WaveForm DataBase) format following PhysioNet schema (extended). | collection-002 | Waveform telemetry capture
Medical imaging data acquired from hospital PACS (Picture Archiving and Communication System) and stored in DICOM format with comprehensive metadata following DICOM schema. | collection-003 | Medical imaging acquisition
Electroencephalography recordings extracted from hospital EEG databases in EDF+ and Persyst formats with metadata following open source schemas. | collection-004 | EEG recording extraction
Multi-center network capabilities to acquire, standardize, tokenize, store, visualize, and label data. All structured clinical data transformed to OMOP Common Data Model. Clinical notes tokenized using OHNLP toolkit. Imaging converted to DICOM. Waveforms standardized to WFDB format. | collection-005 | Standardized data transformation

Acquisition Methods

Description | ID | Name
--- | --- | ---
Demographics, medication administration, procedures, nursing flowsheets, and diagnoses acquired from electronic health records and standardized to OMOP Common Data Model. Controlled access with published OMOP schema metadata. | acquisition-001 | Structured EHR data (OMOP)
Dosing information time-stamped upon each infusion change or dose administration. Stored in OMOP format with controlled access and OMOP schema metadata. | acquisition-002 | Medication administration records
Procedures and diagnoses documented by healthcare providers. Stored in OMOP format with controlled access and OMOP schema metadata. | acquisition-003 | Provider documentation
Nursing documentation captured at high frequency intervals. Stored in OMOP format with controlled access and OMOP schema with extensions for high-frequency data. | acquisition-004 | High-frequency nursing flowsheets
Clinical notes extracted and tokenized using OHNLP (Open Health Natural Language Processing) toolkit. Stored locally except tokens. Controlled access planned with OHNLP open source schema metadata. | acquisition-005 | Clinical notes (tokenized)
Imaging data from PACS systems in DICOM format. De-identification in process. Planned controlled access with published DICOM schema metadata. | acquisition-006 | Medical imaging (DICOM)
Bedside monitor waveform data acquired through gateway/middleware systems. Stored in WFDB format with controlled access and published PhysioNet schema (extended) metadata. | acquisition-007 | Waveform telemetry (WFDB)
EEG recordings from hospital databases in EDF+ and Persyst formats. Extraction in process. Planned controlled access with open source EDF+ and Persyst schema metadata. | acquisition-008 | EEG waveforms

Preprocessing Strategies

Description | ID | Name | Preprocessing Details
--- | --- | --- | ---
All structured electronic health record data standardized to the OMOP (Observational Medical Outcomes Partnership) Common Data Model. Ensures interoperability and enables use of OHDSI (Observational Health Data Sciences and Informatics) tool stack for analysis. | preproc-001 | OMOP Common Data Model transformation | Transformation of source EHR data to OMOP CDM, Standardization of terminology and codes, Mapping to OMOP vocabulary standards, Quality assurance of transformed data, Integration with OHDSI tools for analysis
Clinical notes processed using OHNLP (Open Health Natural Language Processing) toolkit for extraction and tokenization. Protects patient privacy while enabling natural language processing and analysis. | preproc-002 | Clinical note tokenization | Text extraction from clinical notes, OHNLP tokenization pipeline, De-identification of sensitive information, Standardization to OHNLP schema, Local storage (except tokens)
Waveform telemetry data from diverse bedside monitoring systems standardized to WFDB (WaveForm DataBase) format following extended PhysioNet schema. | preproc-003 | Waveform standardization | Conversion from proprietary monitor formats, Standardization to WFDB format, Metadata extraction and schema compliance, Quality checks for waveform integrity, Synchronization with clinical events
DICOM imaging data undergoing de-identification process to remove patient identifiable information while preserving clinical utility and metadata. | preproc-004 | Medical imaging de-identification | DICOM header de-identification, Removal of embedded patient information, Preservation of clinically relevant metadata, DICOM schema compliance verification, Quality assurance of de-identified images
Data transformed using approaches that limit re-identification while maintaining analytical utility. Multiple preprocessing strategies employed to protect patient privacy across all data modalities. | preproc-005 | Data re-identification limitation | Application of de-identification algorithms, Privacy-preserving transformations, Risk assessment for re-identification, Compliance with ethical and legal requirements, Community ethics focus group input

Cleaning Strategies

Cleaning Details | Description | ID | Name
--- | --- | --- | ---
Semantic mapping validation by clinical experts, Standard operating protocol implementation, Cross-site data quality checks, Resolution of institutional variations, Clinical validation SOP for mappings | Data from 14 acquisition centers harmonized through validated semantic mappings and standard operating protocols (SOPs). Ensures consistency and interoperability across diverse institutional EHR systems and clinical practices. | cleaning-001 | Multi-center data harmonization
Schema compliance verification, Metadata completeness checks, Validation against published standards, Error detection and correction, Documentation of schema extensions | All data modalities validated against published metadata schemas (OMOP, DICOM, WFDB, OHNLP, EDF+, Persyst) to ensure compliance and data quality. | cleaning-002 | Metadata schema validation

Labeling Strategies

1. ID
     labeling-001
   Name
     Visualization and annotation environment
   Description
     Custom visualization and annotation environment developed to label data with targets important for prediction tasks in critical care AI applications.
   Labeling Details
     - Interactive visualization tools
     - Annotation interface for clinical experts
     - Labeling of prediction targets
     - Quality control of annotations
     - Documentation of labeling protocols

Maintainers

Description | ID | Maintainer Details | Name
--- | --- | --- | ---
Multi-institutional consortium managing dataset maintenance including data acquisition centers, coordinating teams, and infrastructure development. | maintainer-001 | Massachusetts General Hospital (lead institution), University of Florida (data acquisition and coordination), UT Health Science Center (data acquisition and coordination), Tufts Medicine (data acquisition and coordination), 14 data acquisition centers across United States, Standards team (semantic mappings and validation), Data Acquisition team (extraction and contribution), Tooling team (software development), Project management via GitHub organization (chorus-ai) | CHoRUS Consortium
Active GitHub organization (chorus-ai) housing repositories for software, semantic mappings, standard operating protocols, and project management. 28 repositories with comprehensive documentation and community support. | maintainer-002 | GitHub organization: https://github.com/chorus-ai, Chorus_SOP repository (centralized documentation), chorus-container-apps (Azure deployment), chorus-mapping (semantic mappings), chorus_waveform (waveform tools), privacy_scan_tool (privacy protection), Community discussions and issue tracking | CHoRUS GitHub Organization

Retention Limit

ID
  retention-001
Name
  Long-term dataset retention
Description
  Digital data maintained according to NIH data sharing policies and institutional requirements. Controlled access model ensures long-term availability for research while protecting patient privacy.
Retention Details
  - NIH data sharing policies govern retention
  - Institutional requirements at participating centers
  - Controlled access model for privacy protection
  - Secure enclave infrastructure for data storage
  - Long-term maintenance through CHoRUS Consortium

Sensitive Elements

Description | ID | Name | Sensitive Elements Present | Sensitivity Details
--- | --- | --- | --- | ---
Complete electronic health records including demographics, diagnoses, procedures, medications, nursing documentation, and clinical notes for critically ill patients. Contains protected health information subject to HIPAA and institutional privacy requirements. | sensitive-001 | Clinical and medical data | True | Demographics and patient identifiers (de-identified), Diagnoses and medical conditions, Medication administration records, Procedures and interventions, Clinical notes (tokenized for privacy), Nursing flowsheet documentation
Continuous waveform telemetry and EEG recordings capturing detailed physiological states of critically ill patients. May reveal sensitive health conditions and treatment responses. | sensitive-002 | Physiological monitoring data | True | Continuous cardiac waveforms, Respiratory monitoring data, Hemodynamic measurements, Electroencephalography recordings, High-frequency physiological parameters
Diagnostic imaging studies in DICOM format. Undergoing de-identification to remove embedded patient information while preserving clinical utility. | sensitive-003 | Medical imaging | True | CT scans and X-rays, MRI and other imaging modalities, DICOM metadata (de-identified), Embedded patient information (removed)
Contextual factors including geographic information (distance to hospital) and social determinants of health data. Collected to support health equity research while maintaining patient privacy. | sensitive-004 | Social Determinants of Health | True | Geographic location information, Social determinants of health variables, Contextual factors for equity research, Privacy-preserving transformations applied

External Resources

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
Contributor | CHoRUS Project Website | resource-001 | -
Contributor | CHoRUS GitHub Organization | resource-002 | -
Contributor | Chorus_SOP Documentation | resource-003 | -
Contributor | CHoRUS Developer Documentation | resource-004 | -
Contributor | NIH RePORTER Project Details | resource-005 | -
Contributor | Bridge2AI Program | resource-006 | -
Contributor | AIM-AHEAD Training Partnership | resource-007 | -
Contributor | OHDSI Community | resource-008 | -
Contributor | Published Research | resource-009 | -
Contributor | Contact for Access | resource-010 | -

🚀

## Uses

What (other) tasks could the dataset be used for?

Tasks

Description | ID | Name
--- | --- | ---
Generate data for ML/AI applications aimed at characterizing acute and critical care illness patterns, progression, and outcomes across diverse patient populations and hospital settings. | task-001 | Characterize acute and critical care illness
Enable prediction of complications among patients with acute or critical illness using multi-modal data including structured EHR, waveforms, imaging, and clinical notes. | task-002 | Predict complications in critically ill patients
Support measurement and analysis of treatment response among critically ill patients through high-frequency documentation, medication administration records, and clinical outcomes data. | task-003 | Measure treatment response
Provision a holdout test set accessible for model external validation to aid marketplace adoption of AI-developed models for implementation in acute and critical care settings. | task-004 | External validation for marketplace adoption
Utilize visualization and annotation environment to label data with targets important for prediction tasks in critical care AI applications. | task-005 | Label data for prediction targets

Intended Uses

Description | ID | Name
--- | --- | ---
Primary intended use is development and training of artificial intelligence and machine learning models to characterize acute and critical care illness, predict complications, and measure treatment response in critically ill patients. | use-001 | AI/ML model development for critical care
Provision of holdout test set accessible for model external validation to aid marketplace adoption of AI-developed models for implementation in acute and critical care settings. | use-002 | External validation of AI models
Studies examining health equity, social determinants of health, and disparities in critical care outcomes across diverse patient populations and hospital settings. | use-003 | Research on health equity and disparities
Research aimed at improving clinical care delivery, treatment protocols, and patient outcomes in acute and critical care settings through data-driven insights. | use-004 | Clinical care improvement research
Training and education of next generation of diverse academic and community AI scientists through hands-on experience with real-world critical care datasets. Integration with AIM-AHEAD training programs. | use-005 | Educational and training purposes

Discouraged Uses

Description | ID | Name
--- | --- | ---
As data collection continues through November 2026 and quality assurance processes are ongoing, early dataset versions should be used with awareness of completeness limitations and ongoing expansion. | discouraged-001 | Uses during ongoing data collection
Dataset is for research purposes. Any AI/ML models developed should undergo appropriate clinical validation, regulatory approval, and institutional review before use in patient care or clinical decision-making. | discouraged-002 | Clinical decision-making without validation
Attempts to re-identify patients from de-identified data violate ethical principles, data use agreements, and legal frameworks established for privacy protection. | discouraged-003 | Re-identification attempts

License and Use Terms

ID
  license-001
Name
  CHoRUS Controlled Access License
Description
  Dataset distributed under controlled access requiring institutional email registration and signed licensing agreement. Access granted after review and approval process. Participants must complete registration form with name, email (institutional, not personal), and institution. Once approved, users receive email with access instructions to CHoRUS secure enclave. Contact for access requests: dbold@emory.edu or jared.houghtaling@tuftsmedicine.org.
License Terms
  - Institutional email required for registration
  - Signed licensing agreement required
  - Controlled access through secure enclave
  - Data use agreement specifies permitted uses
  - Prohibition on re-identification attempts
  - Compliance with ethical and legal requirements

📤

## Distribution

How will the dataset be distributed?

License
Controlled Access with Data Use Agreement
🔄

## Maintenance

How will the dataset be maintained?

Updates

ID
  updates-001
Name
  Ongoing data collection and expansion
Description
  Dataset updated continuously as data collection progresses at 14 acquisition centers. As of November 2024, covers 14 hospitals with 23,400 unique admissions. Target exceeds 100,000 critically ill patients. Project timeline extends through November 30, 2026 (with approved no-cost extension). Regular status updates tracked through GitHub project management system. Sites provide updates via GitHub interface or Google Form submissions.
Frequency
  Continuous updates through November 2026
Update Details
  - Ongoing retrospective data collection at 14 sites
  - Current status: 23,400 unique admissions (as of November 2024)
  - Target: 100,000+ critically ill patients
  - Regular site status updates via GitHub/Google Forms
  - GitHub project tracking for deliverables
  - Documentation updates in Chorus_SOP repository
  - Software and tooling continuous development
  - Semantic mapping validation and expansion

👥

## Human Subjects

Does the dataset relate to people?

Human Subject Research

ID
  hsr-001
Name
  CHoRUS Human Subjects Research
Description
  Retrospective data collection from critically ill patients approved through institutional review processes. Community-facing ethics focus groups conducted to determine what data is appropriate for public sharing. Legal framework established for collecting data at scale. Patient-focused efforts determine ethical and legal approaches to manage privacy and bias while accounting for Social Determinants of Health. Project draws expertise from law, ethics, health services, biomedical science, engineering, and scientific journal publications disciplines.
Involves Human Subjects
  True
Ethics Review Board
  - Institutional review boards at 14 data acquisition centers
  - Community-facing ethics focus groups
  - Legal and ethical advisory teams
  - Privacy and accountability review processes

Generated on 2026-04-21 15:34:02 using Bridge2AI Data Sheets Schema