Original Research
Exp. Biol. Med.
Sec. Artificial Intelligence/Machine Learning Applications to Biomedical Research
Volume 250 - 2025 | doi: 10.3389/ebm.2025.10374
This article is part of the IssueProceedings of the 10th Annual Conference of the Arkansas Bioinformatics Consortium (AR-BIC) - Real-World Impact of AIView all 8 articles
A Refined Set of RxNorm Drug Names for Enhancing Unstructured Data Analysis in Drug Safety Surveillance
- National Center for Toxicological Research (FDA), Jefferson, Arkansas, United States
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Adverse drug events are harms associated with drug use, whether the drug is used correctly or incorrectly. Identifying adverse drug events is vital in pharmacovigilance to safeguard public health. Drug safety surveillance can be performed using unstructured data. A comprehensive and accurate list of drug names is essential for effective identification of adverse drug events. While there are numerous sources for drug names, RxNorm is widely recognized as a leading resource.However, its effectiveness for unstructured data analysis in drug safety surveillance has not been thoroughly assessed. To address this, we evaluated the drug names in RxNorm for their suitability in unstructured data analysis and developed a refined set of drug names. Initially, we removed duplicates, the names exceeding 199 characters, and those that only describe administrative details.Drug names with four or fewer characters were analyzed using 18,000 drug-related PubMed abstracts to remove names which rarely appear in unstructured data. The remaining names, which ranged from five to 199 characters, were further refined to exclude those that could lead to inaccurate drug counts in unstructured data analysis. We compared the efficiency and accuracy of the refined set with the original RxNorm set by testing both on the 18,000 drug-related PubMed abstracts. The results showed a decrease in both computational cost and the number of false drug names identified. Further analysis of the removed names revealed that most originated from only one of the 14 sources. Our findings suggest that the refined set can enhance drug identification in unstructured data analysis, thereby improving pharmacovigilance.
Keywords: Adverse drug events, RxNorm, drug safety, drug name, surveillance
Received: 09 Sep 2024; Accepted: 22 Apr 2025.
Copyright: © 2025 Guo, Dong, Liu, Aslam, Patterson and Hong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Huixiao Hong, National Center for Toxicological Research (FDA), Jefferson, 72079, Arkansas, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.