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- W4287776314 abstract "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." @default.
- W4287776314 created "2022-07-26" @default.
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- W4287776314 date "2020-05-21" @default.
- W4287776314 modified "2023-09-23" @default.
- W4287776314 title "Extracting Daily Dosage from Medication Instructions in EHRs: An Automated Approach and Lessons Learned" @default.
- W4287776314 doi "https://doi.org/10.48550/arxiv.2005.10899" @default.
- W4287776314 hasPublicationYear "2020" @default.
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