UCLA

Severity scoring systems are frequently used in hospital intensive care units to assess patient wellness and mortality probability. Accurate mortality predictions are vital to provide appropriate and timely treatments to critical patients. However, commonly used severity scores have been found to make inaccurate mortality predictions. In this paper, we assess and compare four popular severity scores for both discrimination and calibration. We apply logistic regression, random forest, and neural network classification techniques in order to present new mortality prediction models, and compare their performance with pre-existing scores. We also compare the use of a basic set of predictor features that can be easily collected in the ICU environment with an expanded predictor set including laboratory diagnostics. Newly developed models improved on existing severity scores in terms of discrimination, with random forest providing the best results, but often demonstrated poor calibration. The expanded variable set did not improve model performance.