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

How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

39.8K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
39.8K
Nominal Level of Measurement00:56

Nominal Level of Measurement

40.6K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
40.6K
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

35.6K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
35.6K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

444
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
444
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

476
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
476
Real Number Operations01:27

Real Number Operations

405
The concept of real numbers includes all the values that can be represented on a continuous number line. The system began with basic counting values used for enumeration. It later expanded to include values that represent the absence of quantity and opposites of the counting values. When situations required expressing parts of a whole or dividing quantities evenly, values capable of representing such proportions were developed. When written using decimal notation, these values can end or repeat...
405

You might also read

Related Articles

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

Sort by
Same author

<i>History</i> in the Basic Formal Ontology.

CEUR workshop proceedings.·2026
Same author

Refining Substance Use Classification: An Ontological Framework for Enhancing Large-Scale Data Collection.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

Towards Representing Change in the BFO.

Formal ontology in information systems : proceedings of the ... International Conference. FOIS (Conference)·2025
Same author

Semantic Difficulties in FHIR 'Conditions'.

Studies in health technology and informatics·2025
Same author

Fiat Surfaces in the Basic Formal Ontology.

Formal ontology in information systems : proceedings of the ... International Conference. FOIS (Conference)·2024
Same author

Challenges in Realism-Based Ontology Design: a Case Study on Creating an Ontology for Motivational Learning Theories.

CEUR workshop proceedings·2024
Same journal

The Essential Components and Critical Conditions for Success in a Learning Health System in Oncology.

Studies in health technology and informatics·2026
Same journal

Use of Artificial Intelligence in Screening for Adolescent Idiopathic Scoliosis: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Movement Related Biomechanics in Adolescent Idiopathic Scoliosis: A Review of Reviews.

Studies in health technology and informatics·2026
Same journal

The Impact of Surgical Correction of Adolescent Idiopathic Scoliosis Using Posterior Spinal Fusion on Selected Radiological Parameters and Respiratory Function.

Studies in health technology and informatics·2026
Same journal

Acute Effect of Physio-logic® Exercises on Muscle Tone and Stiffness in Adolescent Idiopathic Scoliosis Patients: A Preliminary Study.

Studies in health technology and informatics·2026
Same journal

Effects of Integrated Music and Occupational Therapy on Motor and Autonomic Function in Children with Neurogenic Scoliosis.

Studies in health technology and informatics·2026
See all related articles

Related Experiment Video

Updated: Mar 2, 2026

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

9.8K

A Realism-Based View on Counts in OMOP's Common Data Model.

Werner Ceusters1, Jonathan Blaisure1

  • 1Department of Biomedical Informatics, University at Buffalo, Buffalo NY, USA.

Studies in Health Technology and Informatics
|May 9, 2017
PubMed
Summary
This summary is machine-generated.

Accurate entity counting is crucial for analytics. This study found that the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and its standard condition era algorithm may lead to incorrect entity counts in compatible data repositories.

Keywords:
Common data modelsOMOPrealism-based ontologies

More Related Videos

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.3K
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.8K

Related Experiment Videos

Last Updated: Mar 2, 2026

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

9.8K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.3K
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.8K

Area of Science:

  • Health Informatics
  • Data Modeling
  • Ontology

Background:

  • Accurate entity counting is fundamental for the reliable functioning of healthcare analytics tools.
  • The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is widely used for standardizing observational health data.
  • Ensuring data integrity within the OMOP CDM is critical for valid downstream analyses.

Purpose of the Study:

  • To evaluate the OMOP Common Data Model's specifications for accurate entity counting in compatible data repositories.
  • To identify potential discrepancies between OMOP CDM constructs and realism-based ontological principles for entity representation.
  • To assess the accuracy of a proposed standard algorithm for computing condition eras within the OMOP CDM framework.

Main Methods:

  • Comparative analysis of OMOP CDM constructs (tables, fields, attributes, cardinality constraints, business rules) against realism-based ontologies.
  • Examination of documentation and related literature for OMOP CDM specifications and algorithms.
  • Assessment of a standard algorithm for computing condition eras for potential counting errors.

Main Results:

  • The study identified potential issues within the OMOP CDM's structure that could lead to inaccurate entity counts.
  • The proposed standard algorithm for computing condition eras was found to be a potential source of erroneous counting.
  • Discrepancies were noted between OMOP CDM definitions and the axioms proposed in realism-based ontologies regarding entity types.

Conclusions:

  • The OMOP Common Data Model, in its current specifications, may not inherently guarantee accurate entity counting.
  • The standard algorithm for condition eras requires further scrutiny to prevent miscounts in observational health data.
  • Future work should focus on refining the OMOP CDM and associated algorithms to ensure data integrity for analytics.