2 datasets found
  1. p

    MIMIC-IV-Note: Deidentified free-text clinical notes

    • physionet.org
    Updated Jan 6, 2023
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    Alistair Johnson; Tom Pollard; Steven Horng; Leo Anthony Celi; Roger Mark (2023). MIMIC-IV-Note: Deidentified free-text clinical notes [Dataset]. http://doi.org/10.13026/1n74-ne17
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    Dataset updated
    Jan 6, 2023
    Authors
    Alistair Johnson; Tom Pollard; Steven Horng; Leo Anthony Celi; Roger Mark
    License

    https://github.com/MIT-LCP/license-and-dua/tree/master/draftshttps://github.com/MIT-LCP/license-and-dua/tree/master/drafts

    Description

    The advent of large, open access text databases has driven advances in state-of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data available for NLP has been cited as a significant barrier to the field's progress. Here we describe MIMIC-IV-Note: a collection of deidentified free-text clinical notes for patients included in the MIMIC-IV clinical database. MIMIC-IV-Note contains 331,794 deidentified discharge summaries from 145,915 patients admitted to the hospital and emergency department at the Beth Israel Deaconess Medical Center in Boston, MA, USA. The database also contains 2,321,355 deidentified radiology reports for 237,427 patients. All notes have had protected health information removed in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. All notes are linkable to MIMIC-IV providing important context to the clinical data therein. The database is intended to stimulate research in clinical natural language processing and associated areas.

  2. p

    MIMIC-IV-Note: Deidentified free-text clinical notes

    • physionet.org
    Updated Jan 5, 2023
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    Alistair Johnson; Tom Pollard; Steven Horng; Leo Anthony Celi; Roger Mark (2023). MIMIC-IV-Note: Deidentified free-text clinical notes [Dataset]. http://doi.org/10.13026/0p14-t007
    Explore at:
    Dataset updated
    Jan 5, 2023
    Authors
    Alistair Johnson; Tom Pollard; Steven Horng; Leo Anthony Celi; Roger Mark
    License

    https://github.com/MIT-LCP/license-and-dua/tree/master/draftshttps://github.com/MIT-LCP/license-and-dua/tree/master/drafts

    Description

    The advent of large, open access text databases has driven advances in state-of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data available for NLP has been cited as a significant barrier to the field's progress. Here we describe MIMIC-IV-Note: a collection of deidentified free-text clinical notes for patients included in the MIMIC-IV clinical database. MIMIC-IV-Note contains 357,289 deidentified discharge summaries from 161,403 patients admitted to the hospital and emergency department at the Beth Israel Deaconess Medical Center in Boston, MA, USA. The database also contains 2,471,881 deidentified radiology reports for 256,400 patients. All notes have had protected health information removed in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. All notes are linkable to MIMIC-IV providing important context to the clinical data therein. The database is intended to stimulate research in clinical natural language processing and associated areas.

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Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Alistair Johnson; Tom Pollard; Steven Horng; Leo Anthony Celi; Roger Mark (2023). MIMIC-IV-Note: Deidentified free-text clinical notes [Dataset]. http://doi.org/10.13026/1n74-ne17

MIMIC-IV-Note: Deidentified free-text clinical notes

Explore at:
60 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jan 6, 2023
Authors
Alistair Johnson; Tom Pollard; Steven Horng; Leo Anthony Celi; Roger Mark
License

https://github.com/MIT-LCP/license-and-dua/tree/master/draftshttps://github.com/MIT-LCP/license-and-dua/tree/master/drafts

Description

The advent of large, open access text databases has driven advances in state-of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data available for NLP has been cited as a significant barrier to the field's progress. Here we describe MIMIC-IV-Note: a collection of deidentified free-text clinical notes for patients included in the MIMIC-IV clinical database. MIMIC-IV-Note contains 331,794 deidentified discharge summaries from 145,915 patients admitted to the hospital and emergency department at the Beth Israel Deaconess Medical Center in Boston, MA, USA. The database also contains 2,321,355 deidentified radiology reports for 237,427 patients. All notes have had protected health information removed in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. All notes are linkable to MIMIC-IV providing important context to the clinical data therein. The database is intended to stimulate research in clinical natural language processing and associated areas.

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