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.
@article{arxiv.2005.10899,
title = {Extracting Daily Dosage from Medication Instructions in EHRs: An Automated Approach and Lessons Learned},
author = {Diwakar Mahajan and Jennifer J. Liang and Ching-Huei Tsou},
journal= {arXiv preprint arXiv:2005.10899},
year = {2021}
}