A refined set of RxNorm drug names for enhancing unstructured data analysis in drug safety surveillance

Abstract

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: DrugBank; adverse drug events; database; natural language processing; pharmacovigilance.

FIGURE 1

Study overview. The flowchart illustrates the procedures used to generate and evaluate a refined set of drug names from RxNorm, including extraction of drug names from the RxNorm website, removal of duplicates, filtering false names, discarding names that likely lead to redundant occurrence counts in unstructured data analysis, and evaluating accuracy and efficiency of the refined set.

FIGURE 2

Source distribution of the removed drug names that only originate from a single source for names with four or fewer characters (A), names with five to 199 characters (B), and names with 200 or more characters (C). The y-axes give number of names and x-axes depict name sources. Abbreviations: ATC (Anatomical Therapeutic Chemical Classification System), CVX (Vaccines Administered), DB (DrugBank), GS (Gold Standard Drug Database), MMSL (Micromedex RED BOOK), MMX (Micromedex), MSH (Medical Subject Headings), MTHCMS (CMS Formulary Reference File), MTHSPL (FDA Structured Product Labeling), NDDF (First Databank), RXNORM (RxNorm itself), SNOMED (SNOMED Clinical Terms), USP (United States Pharmacopeia), and VANDF (Veterans Health Administration National Drug File).

FIGURE 3

Comparison of name length between the refined set and the original RxNorm set. The y-axis shows the number of drug names, and the x-axis indicates name length. Name lengths were color coded in red for the refined sets and in blue for the original RxNorm set.

FIGURE 4

Source of original RxNorm drug names that were excluded from the refined set but identified in the PubMed abstracts. The y-axis represents number of drug names and the x-axis depicts sources. Abbreviations: ATC (Anatomical Therapeutic Chemical Classification System), CVX (Vaccines Administered), DB (DrugBank), GS (Gold Standard Drug Database), MMSL (Micromedex RED BOOK), MMX (Micromedex), MSH (Medical Subject Headings), MTHCMS (CMS Formulary Reference File), MTHSPL (FDA Structured Product Labeling), NDDF (First Databank), RXNORM (RxNorm itself), SNOMED (SNOMED Clinical Terms), USP (United States Pharmacopeia), and VANDF (Veterans Health Administration National Drug File).