  Over the past decade the rate of care unit (CU) use in the United States has
been increasing. With an aging population and ever-growing demand for medical
care, effective management of patients' transitions among different care
facilities will prove indispensible for shortening the length of hospital
stays, improving patient outcomes, allocating critical care resources, and
reducing preventable re-admissions. In this paper, we focus on an important
problem of predicting the so-called "patient flow" from longitudinal electronic
health records (EHRs), which has not been explored via existing machine
learning techniques. By treating a sequence of transition events as a point
process, we develop a novel framework for modeling patient flow through various
CUs and jointly predicting patients' destination CUs and duration days. Instead
of learning a generative point process model via maximum likelihood estimation,
we propose a novel discriminative learning algorithm aiming at improving the
prediction of transition events in the case of sparse data. By parameterizing
the proposed model as a mutually-correcting process, we formulate the
estimation problem via generalized linear models, which lends itself to
efficient learning based on alternating direction method of multipliers (ADMM).
Furthermore, we achieve simultaneous feature selection and learning by adding a
group-lasso regularizer to the ADMM algorithm. Additionally, for suppressing
the negative influence of data imbalance on the learning of model, we
synthesize auxiliary training data for the classes with extremely few samples,
and improve the robustness of our learning method accordingly. Testing on
real-world data, we show that our method obtains superior performance in terms
of accuracy of predicting the destination CU transition and duration of each CU
occupancy.
