  Medication timelines have been shown to be effective in helping physicians
visualize complex patient medication information. A key feature in many such
designs is a longitudinal representation of a medication's daily dosage and its
changes over time. However, daily dosage as a discrete value is generally not
provided and needs to be derived from free text instructions (Sig). Existing
works in daily dosage extraction are narrow in scope, targeting dosage
extraction for a single drug from clinical notes. Here, we present an automated
approach to calculate daily dosage for all medications, combining deep
learning-based named entity extractor with lexicon dictionaries and regular
expressions, achieving 0.98 precision and 0.95 recall on an expert-generated
dataset of 1,000 Sigs. We also analyze our expert-generated dataset, discuss
the challenges in understanding the complex information contained in Sigs, and
provide insights to guide future work in the general-purpose daily dosage
calculation task.
