Developing well-calibrated illness severity scores for decision support in the critically ill.

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

SUBMITTER: Cosgriff CV 

PROVIDER: S-EPMC6695410 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature