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We left the Training 2 and Validation data sets intact, however, to compare models and evaluate their performance on a data set that reflects the real time use of the score without the need for complicated weighting schemes. Consistent with our previous work, we included all hospitalizations experienced by the patients in our cohort, ignoring within-patient clustering (random) effects; given the rich data available to us, we found that within-patient clustering showed minimal effects on our results.
Given the operating environment where the model was going to be implemented and the clinical use of the model, the modeling options were restricted to models that could be easily instantiated in Java and that would allow the identification of the specific model components with a substantial contribution to the score at the time when the score exceeded the defined cutoff (an alert).
Some ensemble models included weighted combinations of estimates from various models stratified by the most predictive variables. Our final model was a discrete-time logistic regression equation that is easily programmed into Java for real time scoring.