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Related Concept Videos

Data Validation01:03

Data Validation

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.
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Modeling in Therapy

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Participant Modeling
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Modeling and Similitude

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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Evaluating the effectiveness of modeling principles for data models.

Miguel Humberto Torres-Urquidy1, Amit Acharya, Pedro Hernandez-Cott

  • 1Department of Biomedical Informatics, University of Pittsburgh, PA 15261, USA. humberto@dental.pitt.edu

Studies in Health Technology and Informatics
|April 22, 2009
PubMed
Summary
This summary is machine-generated.

Modeling principles improved information representation in dental records, enhancing data consistency. Expert agreement on mapping dental clinical statements and questions increased significantly after applying these principles.

Related Experiment Videos

Last Updated: Jun 23, 2026

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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Published on: January 31, 2014

Area of Science:

  • Health Informatics
  • Data Modeling
  • Dental Informatics

Background:

  • Harmonizing information representation between terminology-ontology and information models is crucial for data consistency.
  • Dental clinical data presents unique challenges for standardized information representation.

Purpose of the Study:

  • To evaluate the effectiveness of specific modeling principles designed to improve information representation.
  • To assess the impact of these principles on the agreement of experts mapping dental data elements.

Main Methods:

  • Utilized dental clinical statements and sample dental record questions.
  • Experts mapped data elements before and after applying modeling principles.
  • Measured expert agreement using Kappa statistic and compared pre- and post-intervention levels.

Main Results:

  • Initial expert agreement (Kappa) ranged from 0.3 to 0.4.
  • After applying modeling principles, expert agreement increased to 0.5 (p<0.05).
  • The observed increase in agreement, though small, was statistically significant.

Conclusions:

  • The applied modeling principles demonstrate effectiveness in harmonizing information representation.
  • Improved agreement suggests enhanced data consistency and interoperability in dental informatics.
  • Further research can explore broader application of these principles in clinical data management.