  Medications often impose temporal constraints on everyday patient activity.
Violations of such medical temporal constraints (MTCs) lead to a lack of
treatment adherence, in addition to poor health outcomes and increased
healthcare expenses. These MTCs are found in drug usage guidelines (DUGs) in
both patient education materials and clinical texts. Computationally
representing MTCs in DUGs will advance patient-centric healthcare applications
by helping to define safe patient activity patterns. We define a novel taxonomy
of MTCs found in DUGs and develop a novel context-free grammar (CFG) based
model to computationally represent MTCs from unstructured DUGs. Additionally,
we release three new datasets with a combined total of N = 836 DUGs labeled
with normalized MTCs. We develop an in-context learning (ICL) solution for
automatically extracting and normalizing MTCs found in DUGs, achieving an
average F1 score of 0.62 across all datasets. Finally, we rigorously
investigate ICL model performance against a baseline model, across datasets and
MTC types, and through in-depth error analysis.
