Real-time mortality prediction in the Intensive Care Unit
Name
JohnsonAMIA2017.pdf
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387.12 KB
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Author(s)
Johnson, Alistair Edward William
Mark, Roger G
Date Issued
April 2018
Journal
AMIA Annual Symposium Proceedings
Publisher
American Medical Informatics Association
Citation
Johnson, Alistair E. W. and Roger G. Mark. "Real-time mortality prediction in the Intensive Care Unit." AMIA Annual Symposium Proceedings (2017): 994-1003 © 2017 AMIA
Version
Author's final manuscript
Abstract
Real-time prediction of mortality for intensive care unit patients has the potential to provide physicians with a simple and easily interpretable synthesis of patient acuity. Here we extract data from a random time during each patient’s ICU stay. We believe this sampling scheme allows for the application of the model(s) across a future patient’s entire ICU stay. The AUROC of a Gradient Boosting model was high (AUROC=0.920), even though no information about diagnosis or comorbid burden was utilized. We also compare models using data from the first 24 hours of a patient’s stay against published severity of illness scores, and find the Gradient Boosting model greatly outperformed the frequently used Simplified Acute Physiology Score II (AUROC = 0.927 vs. 0.809). We nuance this performance with comparison to the literature, provide our interpretation, and discuss potential avenues for improvement.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977709/
