  Machine learning can be used to make sense of healthcare data. Probabilistic
machine learning models help provide a complete picture of observed data in
healthcare. In this review, we examine how probabilistic machine learning can
advance healthcare. We consider challenges in the predictive model building
pipeline where probabilistic models can be beneficial including calibration and
missing data. Beyond predictive models, we also investigate the utility of
probabilistic machine learning models in phenotyping, in generative models for
clinical use cases, and in reinforcement learning.
