Analyzing electronic health records (EHR) poses significant challenges
because often few samples are available describing a patient's health and, when
available, their information content is highly diverse. The problem we consider
is how to integrate sparsely sampled longitudinal data, missing measurements
informative of the underlying health status and fixed demographic information
to produce estimated survival distributions updated through a patient's follow
up. We propose a nonparametric probabilistic model that generates survival
trajectories from an ensemble of Bayesian trees that learns variable
interactions over time without specifying beforehand the longitudinal process.
We show performance improvements on Primary Biliary Cirrhosis patient data.
Alexis Bellot, Mihaela van der Schaar