Developing well-calibrated illness severity scores for decision support in the critically ill
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Author(s)
Cosgriff, Christopher V.
Celi, Leo Anthony G.
Ko, Stephanie
Sundaresan, Tejas
Armengol de la Hoz, Miguel Ángel
Kaufman, Aaron Russell
Stone, David J
Badawi, Omar
Deliberato, Rodrigo
Date Issued
August 2019
Journal
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Citation
Cosgriff, C.V., Celi, L.A., Ko, S. et al. Developing well-calibrated illness severity scores for decision support in the critically ill. npj Digit. Med. 2, 76 (2019). © 2019 The Author(s)
Version
Final published version
Abstract
Illness severity scores are regularly employed for quality improvement and benchmarking in the intensive care unit, but poor generalization performance, particularly with respect to probability calibration, has limited their use for decision support. These models tend to perform worse in patients at a high risk for mortality. We hypothesized that a sequential modeling approach wherein an initial regression model assigns risk and all patients deemed high risk then have their risk quantified by a second, high-risk-specific, regression model would result in a model with superior calibration across the risk spectrum. We compared this approach to a logistic regression model and a sophisticated machine learning approach, the gradient boosting machine. The sequential approach did not have an effect on the receiver operating characteristic curve or the precision-recall curve but resulted in improved reliability curves. The gradient boosting machine achieved a small improvement in discrimination performance and was similarly calibrated to the sequential models.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
MIT Critical Data (Laboratory)
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41746-019-0153-6
