  Effective management of operating room resources relies on accurate
predictions of surgical case durations. This prediction problem is known to be
particularly difficult in pediatric hospitals due to the extreme variation in
pediatric patient populations. We propose a novel metric for measuring accuracy
of predictions which captures key issues relevant to hospital operations. With
this metric in mind we propose several tree-based prediction models. Some are
automated (they do not require input from surgeons) while others are
semi-automated (they do require input from surgeons). We see that many of our
automated methods generally outperform currently used algorithms and even
achieve the same performance as surgeons. Our semi-automated methods can
outperform surgeons by a significant margin. We gain insights into the
predictive value of different features and suggest avenues of future work.
