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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Critical Care, Critical Data.

Christopher V Cosgriff1,2, Leo Anthony Celi1,3, David J Stone1,4

  • 1MIT Critical Data, Massachusetts Institute of Technology, Cambridge, MA, USA.

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|June 21, 2019
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Summary
This summary is machine-generated.

Big data, machine learning, and artificial intelligence are transforming healthcare, especially critical care. Data availability fuels AI development for precision medicine in the intensive care unit (ICU).

Keywords:
Data analyticscritical caredatabaseintensive care unitsmachine learning

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Critical Care Medicine

Background:

  • Big data, machine learning, and artificial intelligence are increasingly integrated into healthcare.
  • Critical care medicine generates robust, continuously monitored data essential for these technologies.
  • Advancements necessitate reliable data for developing AI-driven clinical applications.

Purpose of the Study:

  • To review the fundamental role of data in advancing critical care.
  • To present progress in AI-supported, data-driven precision critical care medicine.
  • To highlight the potential of big data and AI in the intensive care unit (ICU).

Main Methods:

  • Review of current literature on big data, AI, and machine learning in critical care.
  • Analysis of the development of open databases and collaborative clinical data science.
  • Examination of data-driven applications, including clinical decision support.

Main Results:

  • Data availability has reached critical thresholds for robust AI development in clinical settings.
  • Systematic data capture and analysis enable a move towards precision medicine in the ICU.
  • AI is poised to enhance clinical decision-making and patient care in critical care.

Conclusions:

  • Data is fundamental to the advancement of AI in critical care.
  • AI-supported, data-driven approaches are paving the way for precision critical care medicine.
  • The integration of AI promises to revolutionize the intensive care unit (ICU).