Interleaved Semantic Evaluation

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 is building a publicly available dataset targeting over 100,000 critically ill patients with multi-modal
  data including structured EHR, waveform telemetry, medical imaging, EEG, and clinical notes. All structured
  data is standardized to the OMOP Common Data Model with additional formats (DICOM, WFDB, OHNLP tokenization)
  and 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 August 2025, the dataset covers 14 different hospitals with over
  45,000 unique admissions and includes 50,000 patient admissions from ICU, PICU, and NICU, 1.6 billion
  rows of EHR OMOP data, 7,642 admissions with radiology data, and 23 TB of waveform data.

  '

page: https://chorus4ai.org/

language: en

license: Controlled Access - Data Use Agreement Required (OT2OD032701)

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
- ICU
- intensive care

purposes:
- id: chorus:purpose:1
  name: Improve recovery from acute illness
  description: '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.

    '
- id: chorus:purpose:2
  name: Create AI-ready critical care dataset
  description: '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, expanding AI and Machine Learning to improve recovery from acute illness.

    '
- id: chorus:purpose:3
  name: Establish data standards and tools
  description: '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, DICOM, WFDB, OHNLP).

    '
- id: chorus:purpose:4
  name: Promote diversity and health equity
  description: '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 in partnership with AIM-AHEAD.

    '

tasks:
- id: chorus:task:1
  name: Characterize acute and critical care illness
  description: '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.

    '
- id: chorus:task:2
  name: Predict complications in critically ill patients
  description: 'Enable prediction of complications among patients with acute or critical illness using
    multi-modal data including structured EHR, waveforms, imaging, and clinical notes.

    '
- id: chorus:task:3
  name: Measure treatment response
  description: 'Support measurement and analysis of treatment response among critically ill patients through
    high-frequency documentation, medication administration records, and clinical outcomes data.

    '
- id: chorus:task:4
  name: External validation for AI model marketplace adoption
  description: '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.

    '
- id: chorus:task:5
  name: Label data for prediction targets
  description: 'Utilize visualization and annotation environment to label data with targets important
    for prediction tasks in critical care AI applications.

    '

addressing_gaps:
- id: chorus:gap:1
  name: Lack of diverse multi-center critical care datasets
  description: '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 across
    14 data acquisition centers.

    '
- id: chorus:gap:2
  name: Insufficient data standardization in critical care
  description: '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).

    '
- id: chorus:gap:3
  name: Privacy and bias concerns in clinical AI
  description: '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 to determine what data is appropriate for public sharing.

    '
- id: chorus:gap:4
  name: Limited diversity in AI/ML workforce
  description: '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.

    '

creators:
- id: chorus:creator:1
  name: Eric S. Rosenthal
  description: Contact PI/Project Leader, Massachusetts General Hospital (MGH), Director of MGH Neurosciences
    ICU
- id: chorus:creator:2
  name: Azra Bihorac
  description: Principal Investigator, University of Florida (UF)
- id: chorus:creator:3
  name: Ashley Cordes
  description: Principal Investigator, CHoRUS Consortium
- id: chorus:creator:4
  name: Gilles Clermont
  description: Principal Investigator, CHoRUS Consortium
- id: chorus:creator:5
  name: Gari David Clifford
  description: Principal Investigator, CHoRUS Consortium
- id: chorus:creator:6
  name: Barbara J. Evans
  description: Principal Investigator, CHoRUS Consortium
- id: chorus:creator:7
  name: Xiao Hu
  description: Principal Investigator, CHoRUS Consortium
- id: chorus:creator:8
  name: Rishikesan Kamaleswaran
  description: Principal Investigator, CHoRUS Consortium
- id: chorus:creator:9
  name: Yulia A. Levites Strekalova
  description: Principal Investigator, University of Florida (UF)
- id: chorus:creator:10
  name: Parisa Rashidi
  description: Principal Investigator, University of Florida (UF)
- id: chorus:creator:11
  name: Cynthia Rudin
  description: Principal Investigator, CHoRUS Consortium
- id: chorus:creator:12
  name: Ishan Canty Williams
  description: Principal Investigator, CHoRUS Consortium
- id: chorus:creator:13
  name: Andrew Ewing Williams
  description: Principal Investigator, Tufts Medicine
- id: chorus:creator:14
  name: Xiaoqian Jiang
  description: Program Lead, UT Health Science Center (UTHealth Houston)
- id: chorus:creator:15
  name: Morteza Zabihi
  description: Lecturer (Machine Learning Basics), Massachusetts General Hospital
- id: chorus:creator:16
  name: Zhenhong Hu
  description: Instructor (Python and Version Control), University of Florida
- id: chorus:creator:17
  name: Debora Simmons
  description: Lecturer (Ethics of AI in Clinical Practice), UT Health Science Center
- id: chorus:creator:18
  name: Aliyah Geer
  description: Workshop Lead (Data Schemas in Clinical Cloud), Massachusetts General Hospital
- id: chorus:creator:19
  name: Ciera McCrary
  description: Program Manager, Massachusetts General Hospital (MGH)

funders:
- id: chorus:funder:1
  name: NIH Common Fund Bridge2AI Program
  description: 'Funded through National Institutes of Health grant OT2OD032701 (project number 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 approved no-cost extension). Award notice
    date: September 1, 2022. Assistance Listing Number: 93.310.

    '

instances:
- id: chorus:instance:1
  name: Critically ill patients in ICU settings
  description: 'Individual critically ill patients requiring acute or critical care admitted to intensive
    care units (ICU), pediatric intensive care units (PICU), or neonatal intensive care units (NICU) across
    14 data acquisition centers. As of August 2025, the dataset covers 14 different hospitals with over
    45,000 unique admissions, with 50,000 patient admissions from ICU, PICU, and NICU currently released.
    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 (retrospective
    data collection, ongoing collection through November 2026).

    '

subsets:
- id: chorus:subset:1
  name: Controlled Access Structured EHR Dataset (OMOP)
  description: 'Primary dataset with controlled access requiring data use agreement and licensing. Includes
    OMOP-standardized structured EHR data comprising demographics, medication administration (dosing time-stamped
    upon each infusion change or dose administration), procedures, nursing flowsheets (high-frequency
    documentation), and diagnoses. Available in secure enclave. As of August 2025, contains 1.6 billion
    rows of EHR OMOP data. Registration required with institutional email; all participants must sign
    licensing agreement.

    '
- id: chorus:subset:2
  name: Waveform Telemetry Dataset (WFDB)
  description: 'Waveform telemetry data from bedside monitors (gateway/middleware) standardized to WFDB
    (WaveForm DataBase) format following extended PhysioNet schema. Available in secure enclave with controlled
    access. As of August 2025, contains 23 TB of waveform data. Published metadata schema: PhysioNet schema
    (extended).

    '
- id: chorus:subset:3
  name: Medical Imaging Dataset (DICOM)
  description: 'Imaging data from hospital PACS (Picture Archiving and Communication System) in DICOM
    format with comprehensive metadata following DICOM schema. As of August 2025, 1,000 images are available
    with de-identification in process for the larger cohort. 7,642 admissions have radiology data. Controlled
    access planned.

    '
- id: chorus:subset:4
  name: Clinical Notes Dataset (OHNLP Tokenized)
  description: 'Clinical notes extracted and tokenized using OHNLP (Open Health Natural Language Processing)
    toolkit following open source OHNLP schema. Stored locally at contributing sites (except tokens).
    Controlled access planned. De-identification achieved through tokenization approach.

    '
- id: chorus:subset:5
  name: EEG Waveform Dataset (EDF+ and Persyst)
  description: 'Electroencephalography (EEG) waveform data from hospital databases in EDF+ (European Data
    Format) and Persyst formats. Open source EDF+ and Persyst schema metadata available. Extraction in
    process, planned for controlled access.

    '
- id: chorus:subset:6
  name: Training and Publication Subset
  description: 'Subsets of the dataset currently being used for training activities (AIM-AHEAD Bridge2AI
    for Clinical Care Training Program) and publications as the dataset undergoes expansion and quality
    assurance processes.

    '

sampling_strategies:
- id: chorus:sampling:1
  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.
    Community-facing ethics focus groups conducted to determine what data is appropriate for public sharing.

    '
  is_sample: true
  is_random: false
  is_representative: true
  source_data:
  - Critically ill patients in ICU, PICU, and NICU settings at 14 data acquisition centers across 20 academic
    centers in the United States
  representative_verification:
  - Federated multi-center data collection across 14 hospitals ensures geographic and institutional diversity;
    sampling for balanced and diverse patient populations; inclusion of Social Determinants of Health
    contextual factors

subpopulations:
- id: chorus:subpop:1
  name: Critically ill patients by hospital
  description: 'Patients distributed across 14 different hospitals within the CHoRUS network spanning
    20 academic centers in the United States, ensuring geographic and institutional diversity in critical
    care settings.

    '
- id: chorus:subpop:2
  name: ICU, PICU, and NICU patients
  description: 'Patients admitted to intensive care units (ICU), pediatric intensive care units (PICU),
    and neonatal intensive care units (NICU) across contributing hospitals, with 50,000 patient admissions
    in the current released dataset.

    '

sensitive_elements:
- id: chorus:sensitive:1
  name: Protected health information - clinical EHR data
  description: '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. De-identified before release through OMOP
    transformation and privacy scanning tools.

    '
  sensitive_elements_present: true
  sensitivity_details:
  - Demographics and patient identifiers (de-identified before controlled access release)
  - Diagnoses and medical conditions (OMOP standardized)
  - Medication administration records with dosing timestamps (OMOP standardized)
  - Procedures and clinical interventions documented by providers (OMOP standardized)
  - Clinical notes (tokenized using OHNLP toolkit for privacy protection)
  - Nursing flowsheet documentation at high frequency (OMOP with extensions)
- id: chorus:sensitive:2
  name: Physiological monitoring and EEG waveform data
  description: 'Continuous waveform telemetry from bedside monitors and EEG recordings capturing detailed
    physiological states of critically ill patients. May reveal sensitive health conditions and treatment
    responses. Stored in WFDB format (telemetry) and EDF+/Persyst formats (EEG) with controlled access.

    '
  sensitive_elements_present: true
  sensitivity_details:
  - Continuous cardiac waveforms from bedside monitors (WFDB format, 23 TB)
  - Respiratory monitoring and hemodynamic measurement data
  - Electroencephalography (EEG) recordings from hospital databases
  - High-frequency physiological parameters
- id: chorus:sensitive:3
  name: Medical imaging data
  description: 'Diagnostic imaging studies in DICOM format from hospital PACS systems. De-identification
    in process to remove embedded patient information while preserving clinical utility. As of August
    2025, 1,000 images available with de-id in process for larger cohort (7,642 admissions with radiology
    data total).

    '
  sensitive_elements_present: true
  sensitivity_details:
  - Radiology images (CT scans, X-rays, MRI and other modalities) in DICOM format
  - DICOM metadata (de-identification in process)
  - Embedded patient information being removed through de-identification pipeline
- id: chorus:sensitive:4
  name: Social Determinants of Health data
  description: 'Contextual factors including geographic information (distance to hospital) and social
    determinants of health data. Collected to support health equity research while maintaining patient
    privacy through privacy-preserving transformations.

    '
  sensitive_elements_present: true
  sensitivity_details:
  - Geographic location information (distance to nearest hospital)
  - Social determinants of health variables
  - Contextual equity factors with privacy-preserving transformations applied

collection_mechanisms:
- id: chorus:collection:1
  name: Retrospective EHR extraction
  description: '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. Standardized to OMOP Common Data Model.

    '
- id: chorus:collection:2
  name: Waveform telemetry capture
  description: 'Continuous waveform telemetry data captured from bedside monitors through gateway and
    middleware systems at each contributing hospital. Stored in WFDB (WaveForm DataBase) format following
    extended PhysioNet schema. Contains 23 TB of waveform data as of August 2025.

    '
- id: chorus:collection:3
  name: Medical imaging acquisition from PACS
  description: 'Medical imaging data acquired from hospital Picture Archiving and Communication Systems
    (PACS) and stored in DICOM format with comprehensive metadata following DICOM schema. 7,642 admissions
    have radiology data; 1,000 images currently available.

    '
- id: chorus:collection:4
  name: EEG recording extraction
  description: 'Electroencephalography recordings extracted from hospital EEG databases in EDF+ (European
    Data Format) and Persyst formats with metadata following open source schemas. Extraction in process
    as of August 2025.

    '

acquisition_methods:
- id: chorus:acquisition:1
  name: Structured EHR data via OMOP transformation
  description: 'Demographics, medication administration (dosing time-stamped upon each infusion change
    or dose administration), procedures, nursing flowsheets (high-frequency documentation), and diagnoses
    acquired from electronic health records and standardized to OMOP Common Data Model. Controlled access
    with published OMOP schema metadata. Contains 1.6 billion rows of OMOP data as of August 2025.

    '
- id: chorus:acquisition:2
  name: Clinical notes via OHNLP tokenization
  description: 'Clinical notes extracted from EHR systems and tokenized using OHNLP (Open Health Natural
    Language Processing) toolkit. Stored locally at sites except for tokens. Controlled access planned
    with OHNLP open source schema metadata.

    '
- id: chorus:acquisition:3
  name: Medical imaging via DICOM from PACS
  description: 'Imaging data acquired from hospital PACS systems in DICOM format. De-identification in
    process. Planned controlled access with published DICOM schema metadata.

    '
- id: chorus:acquisition:4
  name: Waveform telemetry via WFDB from bedside monitors
  description: 'Bedside monitor waveform data acquired through gateway and middleware systems. Stored
    in WFDB format with controlled access and published PhysioNet schema (extended) metadata. 23 TB available
    as of August 2025.

    '
- id: chorus:acquisition:5
  name: EEG waveforms from hospital databases
  description: 'EEG recordings from hospital databases in EDF+ and Persyst formats. Extraction in process.
    Planned controlled access with open source EDF+ and Persyst schema metadata.

    '

preprocessing_strategies:
- id: chorus:preproc:1
  name: OMOP Common Data Model transformation
  description: '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. Data from all 14 acquisition centers
    unified through this transformation.

    '
  preprocessing_details:
  - Transformation of source EHR data from diverse institutional formats to OMOP CDM
  - Standardization of terminology and clinical codes to OMOP vocabulary standards
  - Mapping to OMOP vocabulary standards using validated semantic mappings
  - Quality assurance of transformed data against OMOP schema specifications
  - Integration with OHDSI tools for downstream analysis and characterization
  - Generation of characterization reports returned to contributing sites via CHoRUSReports
- id: chorus:preproc:2
  name: Clinical note tokenization via OHNLP
  description: '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 of clinical text data.

    '
  preprocessing_details:
  - Text extraction from clinical notes in source EHR systems
  - OHNLP tokenization pipeline applied to free-text clinical documentation
  - De-identification of sensitive information through tokenization approach
  - Standardization to OHNLP open source schema
  - Local storage of full notes at sites; only tokens available in enclave
- id: chorus:preproc:3
  name: Waveform standardization to WFDB format
  description: 'Waveform telemetry data from diverse bedside monitoring systems standardized to WFDB (WaveForm
    DataBase) format following extended PhysioNet schema. Scripts available in chorus_waveform repository.

    '
  preprocessing_details:
  - Conversion from proprietary bedside monitor formats using gateway/middleware systems
  - Standardization to WFDB format per extended PhysioNet schema
  - Metadata extraction and schema compliance verification
  - Quality checks for waveform integrity and completeness
  - Synchronization with clinical events from OMOP EHR data
- id: chorus:preproc:4
  name: Medical imaging de-identification
  description: 'DICOM imaging data undergoing de-identification process to remove patient identifiable
    information from image metadata while preserving clinical utility and image quality.

    '
  preprocessing_details:
  - DICOM header de-identification to remove embedded patient information
  - Preservation of clinically relevant imaging metadata
  - DICOM schema compliance verification after de-identification
  - Quality assurance of de-identified images for clinical utility
  - Privacy scan tool (privacy_scan_tool) used for medical records privacy scanning
- id: chorus:preproc:5
  name: Re-identification limitation transformations
  description: 'Data transformed using approaches that limit re-identification while maintaining analytical
    utility. Multiple preprocessing strategies employed to protect patient privacy across all data modalities
    per HIPAA and institutional requirements.

    '
  preprocessing_details:
  - Application of de-identification algorithms across all data modalities
  - Privacy-preserving transformations for EHR and imaging data
  - Geocoding via DeGauss (UF-Geocoding tool) for OMOP Location entities
  - Risk assessment and compliance with ethical and legal requirements
  - Community ethics focus group input on appropriate data for public sharing

cleaning_strategies:
- id: chorus:cleaning:1
  name: Multi-center data harmonization with validated semantic mappings
  description: '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. Mappings maintained in chorus-mapping repository with clinical
    validation SOP.

    '
  cleaning_details:
  - Semantic mapping validation by clinical experts using chorus-mapping repository
  - Standard operating protocol (SOP) implementation per Chorus_SOP documentation
  - Cross-site data quality checks through CHoRUSReports characterization reports
  - Resolution of institutional variations in coding and clinical terminology
  - Clinical validation SOP for contributing to mapping efforts
  - Site status tracking via GitHub interface and Google Form submissions
- id: chorus:cleaning:2
  name: Schema compliance validation across all modalities
  description: 'All data modalities validated against published metadata schemas (OMOP, DICOM, WFDB, OHNLP,
    EDF+, Persyst) to ensure compliance and data quality across the multi-modal dataset.

    '
  cleaning_details:
  - Schema compliance verification against OMOP CDM specifications
  - DICOM schema metadata validation for imaging data
  - WFDB/PhysioNet schema validation for waveform data
  - OHNLP open source schema validation for tokenized clinical notes
  - EDF+ and Persyst schema validation for EEG data
  - Documentation of schema extensions for OMOP high-frequency nursing flowsheets

labeling_strategies:
- id: chorus:labeling:1
  name: Visualization and annotation environment for prediction targets
  description: 'Custom visualization and annotation environment developed to label data with targets important
    for prediction tasks in critical care AI applications. Supports labeling for characterizing acute
    illness, predicting complications, and measuring treatment response.

    '
  data_annotation_protocol:
  - Interactive visualization tools for clinical data exploration
  - Annotation interface for clinical expert labeling of prediction targets
  - Labeling of targets for characterization, prediction, and treatment response tasks
  - Quality control of annotations through review processes
  - Documentation of labeling protocols per Chorus_SOP standard operating procedures

intended_uses:
- id: chorus:use:1
  name: AI/ML model development for critical care
  description: '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 across diverse hospital settings.

    '
  examples:
  - Characterizing acute and critical care illness patterns using multi-modal data
  - Predicting complications (e.g., sepsis, respiratory failure) in critically ill patients
  - Measuring treatment response in ICU patients using medication and waveform data
  - Developing clinical deep learning models for critical care AI applications
- id: chorus:use:2
  name: External validation of AI models
  description: '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.

    '
  examples:
  - External validation of sepsis prediction models developed at other institutions
  - Benchmarking AI algorithms for critical care across diverse patient populations
- id: chorus:use:3
  name: Health equity and disparities research
  description: 'Studies examining health equity, social determinants of health, and disparities in critical
    care outcomes across diverse patient populations and hospital settings. Dataset includes contextual
    factors such as geographic distance to nearest hospital.

    '
  examples:
  - Analysis of disparities in critical care outcomes by race, ethnicity, and geography
  - Research on social determinants of health in ICU patient populations
- id: chorus:use:4
  name: Educational and training purposes for AI scientists
  description: 'Training and education of next generation of diverse academic and community AI scientists
    through hands-on experience with real-world critical care datasets. Integrated with AIM-AHEAD Bridge2AI
    for Clinical Care Training Program (Cohorts 1 and 2).

    '
  examples:
  - 'AIM-AHEAD training program for underrepresented trainees (Cohort 1: 2024-2025, Cohort 2: 2025-2026)'
  - Foundational hands-on training using Jupyter Notebooks with Bridge2AI CHoRUS ecosystem
  - Workshops on OHDSI/OMOP common data model and clinical AI
  - Development of practical use cases for AI/ML in clinical care

discouraged_uses:
- id: chorus:discouraged:1
  name: Clinical decision-making without proper validation and regulatory approval
  description: 'Dataset is for research purposes only. AI/ML models developed should undergo appropriate
    clinical validation, regulatory approval, and institutional review before use in patient care or clinical
    decision-making.

    '
  discouragement_details:
  - Models trained on CHoRUS data must undergo independent clinical validation
  - Regulatory approval processes (e.g., FDA clearance) required for clinical use
  - Institutional review board approval needed for clinical deployment
- id: chorus:discouraged:2
  name: Re-identification attempts
  description: 'Attempts to re-identify patients from de-identified data violate ethical principles, data
    use agreements, and legal frameworks established for privacy protection under HIPAA and institutional
    requirements.

    '
  discouragement_details:
  - Re-identification attempts violate the signed data use agreement
  - Prohibited under HIPAA and applicable institutional data privacy regulations
  - Data use agreement explicitly prohibits re-identification efforts
- id: chorus:discouraged:3
  name: Use without awareness of ongoing data collection limitations
  description: '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 (from 45K to target 100K+ admissions).

    '
  discouragement_details:
  - Dataset is actively growing; cohort coverage varies by data modality
  - EEG extraction and full imaging de-identification still in process as of 2025
  - Clinical notes stored locally at sites; only tokens available in enclave

license_and_use_terms:
  id: chorus:license:1
  name: CHoRUS Controlled Access License with Data Use Agreement
  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, institutional email (not personal), and institution. Once approved,
    users receive email with access instructions to CHoRUS secure enclave. For training program access,
    program administrators assist with licensing.

    '
  license_terms:
  - Institutional (.edu) email required for registration
  - All participants must sign a licensing agreement before gaining access to the dataset
  - Controlled access through secure enclave (Azure-based infrastructure)
  - Data use agreement specifies permitted research uses and prohibits re-identification
  - Access request contacts - dbold@emory.edu or jared.houghtaling@tuftsmedicine.org
  - Funded under NIH award OT2OD032701; content is solely responsibility of authors

distribution_formats:
- id: chorus:format:1
  name: OMOP Common Data Model (structured EHR data)
  description: 'Structured electronic health record data distributed in OMOP Common Data Model format
    enabling use of OHDSI tool stack for analysis. Contains 1.6 billion rows. Available in secure enclave
    with controlled access. Published OMOP schema metadata available.

    '
  access_urls:
  - https://chorus4ai.org/
- id: chorus:format:2
  name: WFDB waveform format (bedside monitor telemetry)
  description: 'Waveform telemetry data (23 TB) distributed in WFDB (WaveForm DataBase) format following
    extended PhysioNet schema. Available in secure enclave with controlled access. Published PhysioNet
    schema (extended) metadata available.

    '
  access_urls:
  - https://chorus4ai.org/
- id: chorus:format:3
  name: DICOM format (medical imaging)
  description: 'Medical imaging data (7,642 admissions with radiology; 1,000 images currently available)
    distributed in DICOM format with comprehensive metadata following DICOM schema. De-identification
    in process for larger cohort. Planned controlled access.

    '
  access_urls:
  - https://chorus4ai.org/
- id: chorus:format:4
  name: OHNLP tokenized format (clinical notes)
  description: 'Clinical notes distributed as OHNLP-tokenized text following open source OHNLP schema.
    Stored locally at contributing sites (except tokens). Controlled access planned.

    '
  access_urls:
  - https://chorus4ai.org/
- id: chorus:format:5
  name: EDF+ and Persyst formats (EEG waveforms)
  description: 'EEG waveform data distributed in EDF+ (European Data Format) and Persyst formats following
    open source schemas. Extraction in process, planned for controlled access.

    '
  access_urls:
  - https://chorus4ai.org/

maintainers:
- id: chorus:maintainer:1
  name: CHoRUS Consortium
  description: 'Multi-institutional consortium managing dataset maintenance including 14 data acquisition
    centers, coordinating teams at Massachusetts General Hospital (lead), University of Florida, UT Health
    Science Center, and Tufts Medicine, plus infrastructure development team. Standards, Data Acquisition,
    and Tooling sub-teams manage ongoing data delivery and quality. Contact: cmccrary@mgh.harvard.edu
    (Ciera McCrary, Program Manager, MGH).

    '
  maintainer_details:
  - Massachusetts General Hospital (lead institution, Contact PI Eric S. Rosenthal)
  - University of Florida (data acquisition and coordination, Azra Bihorac, Parisa Rashidi, Yulia Strekalova)
  - UT Health Science Center / UTHealth Houston (Xiaoqian Jiang)
  - Tufts Medicine (Andrew Ewing Williams, Manlik Kwong, Jared Houghtaling)
  - 14 data acquisition centers across United States contributing clinical data extracts
  - Standards team (semantic mappings and validation via chorus-mapping repository)
  - Data Acquisition team (extraction and contribution per Chorus_SOP)
  - Tooling team (software development across chorus-ai GitHub organization)
  - Project management via GitHub organization (chorus-ai) with 28 active repositories
- id: chorus:maintainer:2
  name: CHoRUS GitHub Organization (chorus-ai)
  description: 'Active GitHub organization housing repositories for software, semantic mappings, standard
    operating protocols, and project management. 28 repositories with comprehensive documentation and
    community support. Licensed under MIT License (GitHub organization).

    '
  maintainer_details:
  - GitHub organization at https://github.com/chorus-ai
  - Chorus_SOP repository - centralized SOP documentation site (Apache-2.0 license)
  - chorus-container-apps - Azure deployment infrastructure (JavaScript)
  - chorus-mapping - semantic mappings repository
  - chorus_waveform - waveform documentation and conversion scripts (MIT license)
  - privacy_scan_tool - privacy scan tool for medical records (Python)
  - CHoRUSReports - characterization reports returned to contributing sites (R)
  - chorus-extract-upload - tools to create and upload CHoRUS data extract (MIT license)
  - UF-Geocoding - open source code to geocode OMOP Location entities via DeGauss
  - Community discussions and issue tracking for Standards and Data Acquisition teams

updates:
  id: chorus:updates:1
  name: Ongoing data collection and continuous expansion
  description: 'Dataset updated continuously as data collection progresses at 14 acquisition centers.
    As of August 2025, covers 14 hospitals with over 45,000 unique admissions; current released dataset
    includes 50,000 patient admissions (ICU, PICU, NICU) and 1.6 billion rows of EHR OMOP data. Target
    exceeds 100,000 critically ill patients. Project timeline extends through November 30, 2026 (approved
    no-cost extension). Regular status updates tracked through GitHub project management system via GitHub
    interface or Google Form submissions. Sites statuses tracked in Standards Project and Data Acquisition
    Project.

    '
  frequency: Continuous updates through November 30, 2026
  update_details:
  - Ongoing retrospective data collection at 14 sites through project end date
  - Current status (August 2025) - 45K+ unique admissions; 50K released (ICU, PICU, NICU)
  - Target - 100,000+ critically ill patients across 9 data modalities
  - Regular site status updates via GitHub interface or Google Form submissions
  - GitHub project tracking for deliverables and task dependencies
  - Documentation updates maintained in Chorus_SOP repository
  - Software and tooling continuous development across chorus-ai GitHub organization
  - Semantic mapping validation and expansion via chorus-mapping repository
  - EEG extraction and full imaging de-identification in progress

retention_limit:
  id: chorus:retention:1
  name: Long-term dataset retention per NIH data sharing policies
  description: 'Digital data maintained according to NIH data sharing policies and institutional requirements
    at participating centers. Controlled access model ensures long-term availability for research while
    protecting patient privacy. Funded under NIH award OT2OD032701 with project end date of November 30,
    2026.

    '
  retention_details:
  - NIH data sharing policies govern long-term retention requirements
  - Institutional requirements at 14 participating data acquisition centers apply
  - Controlled access model via secure enclave for ongoing privacy protection
  - Long-term maintenance through CHoRUS Consortium and associated institutions

human_subject_research:
  id: chorus:hsr:1
  name: CHoRUS Human Subjects Research
  description: 'Retrospective data collection from critically ill patients (ICU, PICU, NICU) approved
    through institutional review processes at 14 data acquisition centers. 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 includes expertise from
    law, ethics, health services, biomedical science, engineering, and scientific journal publications
    disciplines. Ethics of AI component addressed through AIM-AHEAD training curriculum (safety, risk,
    and legal considerations; IRB, HIPAA/GDPR compliance for OMOP/FHIR data).

    '
  involves_human_subjects: true
  ethics_review_board:
  - Institutional review boards at 14 data acquisition centers across United States
  - Community-facing ethics focus groups determining appropriate data for public sharing
  - Legal and ethical advisory teams including law and ethics discipline experts
  - Privacy and accountability review processes per project three-pillar structure (Data, Ethics, People)
  regulatory_compliance:
  - HIPAA (Health Insurance Portability and Accountability Act) compliance for protected health information
  - 45 CFR 46 (Common Rule) for human subjects research protections
  - Institutional data privacy regulations at each of the 14 contributing sites
  - NIH Common Fund Bridge2AI program ethical and trustworthy AI requirements
  - IRB protocol drafting and HIPAA/GDPR compliance guidance provided in training curriculum

external_resources:
- id: chorus:resource:1
  name: CHoRUS Project Website
  description: Official project website with dataset overview, team information, project components, and
    access instructions
  external_resources:
  - https://chorus4ai.org/
- id: chorus:resource:2
  name: CHoRUS GitHub Organization
  description: Comprehensive GitHub organization with 28 repositories including software, documentation,
    SOPs, and tooling
  external_resources:
  - https://github.com/chorus-ai
- id: chorus:resource:3
  name: Chorus_SOP Documentation Site
  description: Centralized standard operating protocol documentation with interactive workflow diagrams
    for data extraction and contribution
  external_resources:
  - https://github.com/chorus-ai/Chorus_SOP
- id: chorus:resource:4
  name: NIH RePORTER Project Details
  description: Federal grant information and project details from NIH Research Portfolio Online Reporting
    Tools for grant 1OT2OD032701-01
  external_resources:
  - https://reporter.nih.gov/project-details/10472824
- id: chorus:resource:5
  name: Bridge2AI Program
  description: Parent NIH Common Fund program supporting AI-ready biomedical datasets across four data
    generation projects
  external_resources:
  - https://bridge2ai.org/
  - https://bridge2ai.org/chorus
- id: chorus:resource:6
  name: AIM-AHEAD Bridge2AI Training Program
  description: Partnership with AIM-AHEAD for Bridge2AI Clinical Care Training Program (Cohort 1 and Cohort
    2) providing AI/ML training for underrepresented trainees
  external_resources:
  - https://aim-ahead.net/
- id: chorus:resource:7
  name: OHDSI Community and OMOP CDM
  description: Observational Health Data Sciences and Informatics community supporting OMOP Common Data
    Model used for CHoRUS structured EHR data
  external_resources:
  - https://www.ohdsi.org/
- id: chorus:resource:8
  name: Published Research (Neurocritical Care)
  description: Peer-reviewed publication documenting CHoRUS dataset methodology and design in Neurocritical
    Care journal
  external_resources:
  - https://doi.org/10.1007/s12028-024-02007