  Clinical decision support tools rooted in machine learning and optimization
can provide significant value to healthcare providers, including through better
management of intensive care units. In particular, it is important that the
patient discharge task addresses the nuanced trade-off between decreasing a
patient's length of stay (and associated hospitalization costs) and the risk of
readmission or even death following the discharge decision. This work
introduces an end-to-end general framework for capturing this trade-off to
recommend optimal discharge timing decisions given a patient's electronic
health records. A data-driven approach is used to derive a parsimonious,
discrete state space representation that captures a patient's physiological
condition. Based on this model and a given cost function, an infinite-horizon
discounted Markov decision process is formulated and solved numerically to
compute an optimal discharge policy, whose value is assessed using off-policy
evaluation strategies. Extensive numerical experiments are performed to
validate the proposed framework using real-life intensive care unit patient
data.
