@inproceedings{sutton-etal-2026-fast,
    title = "Fast, Accurate, and Local Conversion of {MIMIC}-{IV} to {OMOP} with {DBT}",
    author = "Sutton, Adam  and
      Moller-Grell, Niko  and
      Searle, Thomas  and
      Dobson, Richard",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.80/",
    pages = "992--996",
    ISBN = "979-8-89176-434-7",
    abstract = "dbt mimic omop is a free, open-source resource that converts the MIMIC-IV dataset to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) format on consumer level hardware. CDM approaches are increasingly adopted in both industry and academia due to the need for interoperability and reproducibility, including in clinical NLP tasks such as cohort selection, information extraction, and retrieval-augmented generation. The MIMIC-IV database is among the most widely used critical care research datasets, yet existing pipelines to transform it to OMOP depend on enterprise database infrastructure and complex orchestration, limiting accessibility for practitioners and resource-constrained researchers. We further integrate free-text clinical notes (195.6M clinical annotations) and chest radiographs into the OMOP note nlp and imaging extension tables, making all MIMIC-IV modalities (structured data, free-text, and imaging) accessible through a common data model. This resource generates a more comprehensive dataset than existing alternatives and is intended to be used to aid in system development, testing, and evaluation."
}