  Predicting discharge medications right after a patient being admitted is an
important clinical decision, which provides physicians with guidance on what
type of medication regimen to plan for and what possible changes on initial
medication may occur during an inpatient stay. It also facilitates medication
reconciliation process with easy detection of medication discrepancy at
discharge time to improve patient safety. However, since the information
available upon admission is limited and patients' condition may evolve during
an inpatient stay, these predictions could be a difficult decision for
physicians to make. In this work, we investigate how to leverage deep learning
technologies to assist physicians in predicting discharge medications based on
information documented in the admission note. We build a convolutional neural
network which takes an admission note as input and predicts the medications
placed on the patient at discharge time. Our method is able to distill semantic
patterns from unstructured and noisy texts, and is capable of capturing the
pharmacological correlations among medications. We evaluate our method on 25K
patient visits and compare with 4 strong baselines. Our methods demonstrate a
20% increase in macro-averaged F1 score than the best baseline.
