  We develop a novel algorithm, Predictive Hierarchical Clustering (PHC), for
agglomerative hierarchical clustering of current procedural terminology (CPT)
codes. Our predictive hierarchical clustering aims to cluster subgroups, not
individual observations, found within our data, such that the clusters
discovered result in optimal performance of a classification model. Therefore,
merges are chosen based on a Bayesian hypothesis test, which chooses pairings
of the subgroups that result in the best model fit, as measured by held out
predictive likelihoods. We place a Dirichlet prior on the probability of
merging clusters, allowing us to adjust the size and sparsity of clusters. The
motivation is to predict patient-specific surgical outcomes using data from ACS
NSQIP (American College of Surgeon's National Surgical Quality Improvement
Program). An important predictor of surgical outcomes is the actual surgical
procedure performed as described by a CPT code. We use PHC to cluster CPT
codes, represented as subgroups, together in a way that enables us to better
predict patient-specific outcomes compared to currently used clusters based on
clinical judgment.
