Modern applications of survival analysis increasingly involve time-dependent
covariates. In healthcare settings, such covariates provide dynamic patient
histories that can be used to assess health risks in realtime by tracking the
hazard function. Hazard learning is thus particularly useful in healthcare
analytics, and the open-source package BoXHED 1.0 provides the first
implementation of a gradient boosted hazard estimator that is fully
nonparametric. This paper introduces BoXHED 2.0, a quantum leap over BoXHED 1.0
in several ways. Crucially, BoXHED 2.0 can deal with survival data that goes
far beyond right-censoring and it also supports recurring events. To our
knowledge, this is the only nonparametric machine learning implementation that
is able to do so. Another major improvement is that BoXHED 2.0 is orders of
magnitude more scalable, due in part to a novel data preprocessing step that
sidesteps the need for explicit quadrature when dealing with time-dependent
covariates. BoXHED 2.0 supports the use of GPUs and multicore CPUs, and is
available from GitHub: www.github.com/BoXHED.Comment: 12 page