  Electronic Health Record (EHR) systems provide critical, rich and valuable
information at high frequency. One of the most exciting applications of EHR
data is in developing a real-time mortality warning system with tools from
survival analysis. However, most of the survival analysis methods used recently
are based on (semi)parametric models using static covariates. These models do
not take advantage of the information conveyed by the time-varying EHR data. In
this work, we present an application of a highly scalable survival analysis
method, BoXHED 2.0 to develop a real-time in-ICU mortality warning indicator
based on the MIMIC IV data set. Importantly, BoXHED can incorporate
time-dependent covariates in a fully nonparametric manner and is backed by
theory. Our in-ICU mortality model achieves an AUC-PRC of 0.41 and AUC-ROC of
0.83 out of sample, demonstrating the benefit of real-time monitoring.
