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

Ensemble Prediction of Time to Event Outcomes with Competing Risks: A Case Study of Surgical Complications in Crohn's Disease