Prediction on critically ill patients: The role of "big data".

Journal: Journal of critical care
Published Date: Jul 23, 2020

Abstract

Accurate outcome prediction in Intensive Care Units (ICUs) would allow for better treatment planning, risk adjustment of study populations, and overall improvements in patient care. In the past, prognostic models have focused on mortality using simple ordinal severity of illness scores which could be tabulated manually by a human. With the improvements in computing power and proliferation of electronic medical records, entirely new approaches have become possible. Here we review the latest advances in outcome prediction, paying close attention to methods which are widely applicable and provide a high-level overview of the challenges the field currently faces.

Authors

  • Lucas Bulgarelli
    MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, USA; Big Data Analytics Department, Hospital Israelita Albert Einstein, São Paulo, Brazil. Electronic address: lucas1@mit.edu.
  • Rodrigo Octávio Deliberato
    MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, USA; Department of Clinical Data Science Research, Endpoint Health, Inc., USA.
  • Alistair E W Johnson

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