  We develop a novel algorithm to predict the occurrence of major abdominal
surgery within 5 years following Crohn's disease diagnosis using a panel of 29
baseline covariates from the Swedish population registers. We model
pseudo-observations based on the Aalen-Johansen estimator of the cause-specific
cumulative incidence with an ensemble of modern machine learning approaches.
Pseudo-observation pre-processing easily extends all existing or new machine
learning procedures to right-censored event history data. We propose
pseudo-observation based estimators for the area under the time varying ROC
curve, for optimizing the ensemble, and the predictiveness curve, for
evaluating and summarizing predictive performance.
