Boosted Trees for Risk Prognosis

We present a new approach to ensemble learning for risk prognosis in heterogeneous medical populations. Our aim is to improve overall prognosis by focusing on under-represented patient subgroups with an atypical disease presentation; with current prognostic tools, these subgroups are being consistently mis-estimated. Our method proceeds sequentially by learning nonparametric survival estimators which iteratively learn to improve predictions of previously misdiagnosed patients a process called boosting. This results in fully nonparametric survival estimates, that is, constrained neither by assumptions regarding the baseline hazard nor assumptions regarding the underlying covariate interactions and thus differentiating our approach from existing boosting methods for survival analysis. In addition, our approach yields a measure of the relative covariate importance that accurately identifies relevant covariates within complex survival dynamics, thereby informing further medical understanding of disease interactions. We study the properties of our approach on a variety of heterogeneous medical datasets, demonstrating significant performance improvements over existing survival and ensemble methods.