Systematic Review and Comparison of Publicly Available ICU Data Sets-A Decision Guide for Clinicians and Data Scientists.

Journal: Critical care medicine
PMID: 35234175

Abstract

OBJECTIVE: As data science and artificial intelligence continue to rapidly gain traction, the publication of freely available ICU datasets has become invaluable to propel data-driven clinical research. In this guide for clinicians and researchers, we aim to: 1) systematically search and identify all publicly available adult clinical ICU datasets, 2) compare their characteristics, data quality, and richness and critically appraise their strengths and weaknesses, and 3) provide researchers with suggestions, which datasets are appropriate for answering their clinical question.

Authors

  • Christopher M Sauer
    Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Tariq A Dam
    Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Leo A Celi
    Beth Israel Deaconess Medical Center, Pulmonary Division and Harvard Medical School, Boston, MA 02215, USA.
  • Martin Faltys
    Department of Intensive Care Medicine, University Hospital, University of Bern, Bern, Switzerland.
  • Miguel A A de la Hoz
    Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA.
  • Lasith Adhikari
    Department of Medicine, University of Florida, Gainesville, FL, 32611, USA.
  • Kirsten A Ziesemer
    University Library, Vrije Universiteit, Amsterdam, The Netherlands.
  • Armand Girbes
    Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Patrick J Thoral
    Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Paul Elbers
    Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.

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