  Deep learning is revolutionizing predictive healthcare, including
recommending medications to patients with complex health conditions. Existing
approaches focus on predicting all medications for the current visit, which
often overlaps with medications from previous visits. A more clinically
relevant task is to identify medication changes.
  In this paper, we propose a new recurrent residual network, named MICRON, for
medication change prediction. MICRON takes the changes in patient health
records as input and learns to update a hidden medication vector and the
medication set recurrently with a reconstruction design. The medication vector
is like the memory cell that encodes longitudinal information of medications.
Unlike traditional methods that require the entire patient history for
prediction, MICRON has a residual-based inference that allows for sequential
updating based only on new patient features (e.g., new diagnoses in the recent
visit) more efficiently.
  We evaluated MICRON on real inpatient and outpatient datasets. MICRON
achieves 3.5% and 7.8% relative improvements over the best baseline in F1
score, respectively. MICRON also requires fewer parameters, which significantly
reduces the training time to 38.3s per epoch with 1.5x speed-up.
