2020/05/21 by Diwakar Mahajan, Mahajan, Diwakar, Jennifer J. Liang +3 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Health Professions · Mathematics · Psychology · #Advanced Text Analysis Techniques #Artificial intelligence #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #Computer science #Electronic Health Records Systems #Engineering #Extractor #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Lexicon #Machine Learning in Healthcare #Machine learning #Mathematics #Natural language processing #Psychology #Recall #Relevance (law) #Scope (computer science) #Statistics #Task (project management) #Timeline #Topic Modeling #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.2005.10899
published in arXiv (Cornell University) (Cornell University) · 10 pages, 4 figures, 9 tables
openalex publication_date 2020/05/21 · arxiv created 2021/10/28 · arxiv updated 2021/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.