Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Determination of Renal Drug Clearance: Graphical and Midpoint Methods01:07

Determination of Renal Drug Clearance: Graphical and Midpoint Methods

Renal clearance, a crucial parameter in pharmacokinetics, can be determined using two different methods: the graphical method and the midpoint method. These methods provide insights into the rate of drug excretion by the kidneys and aid in assessing renal function.
The graphical method involves plotting the rate of drug excretion in urine against the plasma drug concentration. By analyzing the graph, the clearance can be calculated and obtained. Drugs rapidly excreted by the kidneys exhibit a...
The R Chart01:02

The R Chart

In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
Dosage Regimen Designs: Nomograms and Tabulations01:23

Dosage Regimen Designs: Nomograms and Tabulations

Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...
Dose Response Curve: Conventional Versus Nonmonotonic01:21

Dose Response Curve: Conventional Versus Nonmonotonic

The correlation between a drug's dosage and its impact on a biological system is a cornerstone of pharmacology and toxicology. Conventional dose–response curves, which include graded and quantal relationships, are key to this understanding. Graded dose–response curves depict the spectrum of a biological reaction to different doses within an individual, indicating that as the drug dosage increases, so does the intensity of the response. On the other hand, quantal dose–response relationships...
Interpreting R Charts01:22

Interpreting R Charts

R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum values—of a sample...
Ogive Graph01:07

Ogive Graph

An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this type...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning.

Journal of the American Medical Informatics Association : JAMIA·2023
Same author

A practical strategy to use the ICD-11 for morbidity coding in the United States without a clinical modification.

Journal of the American Medical Informatics Association : JAMIA·2023
Same author

A deep learning approach to identify missing is-a relations in SNOMED CT.

Journal of the American Medical Informatics Association : JAMIA·2022
Same author

Comparing the representation of medicinal products in RxNorm and SNOMED CT - Consequences on interoperability.

CEUR workshop proceedings·2022
Same author

The New SNOMED CT International Medicinal Product Model.

CEUR workshop proceedings·2022
Same author

Siamese KG-LSTM: A deep learning model for enriching UMLS Metathesaurus synonymy.

The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering·2022
Same journal

Causal intervention validation of gene regulatory signals in scGPT.

Journal of biomedical informatics·2026
Same journal

CoAff-DTI: Fine-grained drug-target interaction prediction using pre-trained language models and affinity-guided mechanisms.

Journal of biomedical informatics·2026
Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026
Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026
Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026
Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

A graph-based approach to auditing RxNorm.

Olivier Bodenreider1, Lee B Peters

  • 1Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD 20894, USA. olivier@nlm.nih.gov

Journal of Biomedical Informatics
|April 28, 2009
PubMed
Summary
This summary is machine-generated.

This study audited RxNorm, a clinical drug nomenclature, finding 24% of paths inconsistent. The auditing method identified errors, with many corrected in subsequent RxNorm versions, improving drug data accuracy.

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Related Experiment Videos

Last Updated: Jun 23, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Medical Informatics
  • Pharmacology
  • Data Quality Assurance

Background:

  • RxNorm is a standardized nomenclature for clinical drug entities developed by the National Library of Medicine.
  • Ensuring the consistency and completeness of drug nomenclature is crucial for accurate clinical decision-making and data interoperability.
  • Previous quality assurance mechanisms in RxNorm may not have detected all relational inconsistencies.

Purpose of the Study:

  • To audit the relations within RxNorm for consistency and completeness.
  • To systematically analyze the graph of RxNorm concepts and relationships.
  • To identify and report inconsistencies in the RxNorm nomenclature.

Main Methods:

  • Normalized the representation of multi-ingredient drugs for compatibility with single-ingredient drugs.
  • Computed and instantiated all meaningful paths within the RxNorm type graph.
  • Automatically compared alternate paths and manually inspected them for inconsistencies.

Main Results:

  • Identified 115 meaningful paths, grouped into 28 sets based on start and end nodes.
  • Found 47% of alternate path groups (9 out of 19) exhibited inconsistencies.
  • Detected 348 inconsistencies in the April 2008 RxNorm version, with 62% corrected by January 2009.

Conclusions:

  • Inconsistencies included missing nodes (93), missing links (17), and extraneous links (237).
  • The auditing method effectively identified errors missed by existing quality assurance processes.
  • Recommendations were provided for the future development of RxNorm to enhance data integrity.