• Lucas Bulgarelli, Rodrigo Octávio Deliberato, and Johnson Alistair E W AEW MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Camb.
• 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.
• J Crit Care. 2020 Dec 1; 60: 64-68.

AbstractAccurate 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.Copyright © 2020 Elsevier Inc. All rights reserved.

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