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

Data Validation01:03

Data Validation

7.0K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
7.0K
Data Validation01:15

Data Validation

2.7K
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
2.7K
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

2.9K
A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
2.9K
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

966
The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
966
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

4.1K
Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
4.1K
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

2.2K
The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...
2.2K

You might also read

Related Articles

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

Sort by
Same author

A knowledge graph-based data harmonization framework for secondary data reuse.

Computer methods and programs in biomedicine·2023
Same author

Improving the Quality and Utility of Electronic Health Record Data through Ontologies.

Standards (Basel, Switzerland)·2023
Same author

Supporting SNOMED CT postcoordination with knowledge graph embeddings.

Journal of biomedical informatics·2023
Same author

Towards a Semantic Data Harmonization Federated Infrastructure.

Studies in health technology and informatics·2021
Same author

Microbial volatile organic compounds in intra-kingdom and inter-kingdom interactions.

Nature reviews. Microbiology·2021
Same author

A novel terpene synthase controls differences in anti-aphrodisiac pheromone production between closely related Heliconius butterflies.

PLoS biology·2021

Related Experiment Video

Updated: Feb 19, 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

Validating EHR clinical models using ontology patterns.

Catalina Martínez-Costa1, Stefan Schulz1

  • 1Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerplatz 2, 8036 Graz, Austria.

Journal of Biomedical Informatics
|November 9, 2017
PubMed
Summary

Ontology design patterns make clinical models explicit and can validate electronic health record (EHR) data. This study used SHACL to find errors in Clinical Information Modelling Initiative (CIMI) models, improving data quality.

Keywords:
Clinical modelsData shapesOntology design patternsSHACLSemantic interoperability

More Related Videos

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.6K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.2K

Related Experiment Videos

Last Updated: Feb 19, 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 Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.6K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.2K

Area of Science:

  • Health Informatics
  • Knowledge Representation
  • Semantic Web Technologies

Background:

  • Clinical models in electronic health records (EHRs) lack formal semantic constraints.
  • Existing clinical models rely on vague information models, leading to potential errors.
  • Explicit semantics are needed for robust clinical data structuring.

Purpose of the Study:

  • To advocate for ontology design patterns (ODPs) to formalize clinical model semantics.
  • To demonstrate the use of ODPs with SHACL for validating clinical models.
  • To identify modeling and terminology binding errors in Clinical Information Modelling Initiative (CIMI) models.

Main Methods:

  • Utilized SHACL (Shapes Constraint Language) for RDF graph validation.
  • Developed two SHACL ontology design patterns.
  • Manually mapped six CIMI clinical models (in RDF) to the SHACL patterns.
  • Employed a Java-based SHACL implementation for validation.

Main Results:

  • Ontology design patterns successfully detected errors in CIMI clinical models.
  • At least eleven modeling and terminology binding errors were identified within the tested CIMI models.
  • SHACL, adhering to the Closed World Assumption, proved suitable for clinical model validation.

Conclusions:

  • Ontology design patterns serve as effective tools for both modeling and validating clinical data.
  • The proposed approach enhances the semantic explicitness and accuracy of EHR clinical models.
  • Formal validation using ODPs and SHACL can significantly improve clinical data quality and interoperability.