  An accurate and detailed account of patient medications, including medication
changes within the patient timeline, is essential for healthcare providers to
provide appropriate patient care. Healthcare providers or the patients
themselves may initiate changes to patient medication. Medication changes take
many forms, including prescribed medication and associated dosage modification.
These changes provide information about the overall health of the patient and
the rationale that led to the current care. Future care can then build on the
resulting state of the patient. This work explores the automatic extraction of
medication change information from free-text clinical notes. The Contextual
Medication Event Dataset (CMED) is a corpus of clinical notes with annotations
that characterize medication changes through multiple change-related
attributes, including the type of change (start, stop, increase, etc.),
initiator of the change, temporality, change likelihood, and negation. Using
CMED, we identify medication mentions in clinical text and propose three novel
high-performing BERT-based systems that resolve the annotated medication change
characteristics. We demonstrate that our proposed systems improve medication
change classification performance over the initial work exploring CMED.

Computation and Language