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      MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports

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          Abstract

          Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient’s chest, but requires specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. Here we describe MIMIC-CXR, a large dataset of 227,835 imaging studies for 65,379 patients presenting to the Beth Israel Deaconess Medical Center Emergency Department between 2011–2016. Each imaging study can contain one or more images, usually a frontal view and a lateral view. A total of 377,110 images are available in the dataset. Studies are made available with a semi-structured free-text radiology report that describes the radiological findings of the images, written by a practicing radiologist contemporaneously during routine clinical care. All images and reports have been de-identified to protect patient privacy. The dataset is made freely available to facilitate and encourage a wide range of research in computer vision, natural language processing, and clinical data mining.

          Abstract

          Measurement(s) Radiograph • investigation results report
          Technology Type(s) Chest Radiography • digital curation • Radiologist
          Sample Characteristic - Organism Homo sapiens

          Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.10303823

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          Most cited references28

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          MIMIC-III, a freely accessible critical care database

          MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.
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            IPython: A System for Interactive Scientific Computing

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              ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

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                Author and article information

                Contributors
                aewj@mit.edu
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                12 December 2019
                12 December 2019
                2019
                : 6
                : 317
                Affiliations
                [1 ]ISNI 0000 0001 2341 2786, GRID grid.116068.8, Institute of Medical Engineering & Science, , Massachusetts Institute of Technology, ; Cambridge, MA USA
                [2 ]ISNI 0000 0000 9011 8547, GRID grid.239395.7, Department of Radiology, , Beth Israel Deaconess Medical Center, ; Boston, MA USA
                [3 ]ISNI 0000 0000 9011 8547, GRID grid.239395.7, Department of Emergency Medicine, , Beth Israel Deaconess Medical Center, ; Boston, MA USA
                [4 ]ISNI 0000000419368956, GRID grid.168010.e, Department of Radiology, , Stanford University, ; Palo Alto, CA USA
                [5 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Biomedical Informatics, , Harvard Medical School, ; Boston, MA USA
                Author information
                http://orcid.org/0000-0002-8735-3014
                http://orcid.org/0000-0002-6318-2978
                Article
                322
                10.1038/s41597-019-0322-0
                6908718
                31831740
                c714b1f2-cff5-448e-b125-dda939c7b850
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.

                History
                : 24 April 2019
                : 11 November 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: NIH-R01-EB017205
                Award ID: NIH-R01-EB017205
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
                Funded by: FundRef https://doi.org/10.13039/100004320, Philips;
                Categories
                Data Descriptor
                Custom metadata
                © The Author(s) 2019

                translational research,radiography
                translational research, radiography

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