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Related Experiment Video

Updated: Jan 22, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Towards a content agnostic computable knowledge repository for data quality assessment.

Naresh Sundar Rajan1, Ramkiran Gouripeddi1, Peter Mo1

  • 1Department of Biomedical Informatics, Center for Clinical and Translational Sciences (CCTS) Biomedical Informatics Core, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT 84108-3514, USA.

Computer Methods and Programs in Biomedicine
|July 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a computable data quality knowledge repository for automated data quality assessments. It enables scalable and reproducible data quality evaluations across diverse health data sources.

Keywords:
Data Quality Metadata RepositoryData quality assessmentData quality dimensionsData quality frameworkKnowledge representation

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Area of Science:

  • Data Science
  • Information Science
  • Health Informatics

Background:

  • Numerous data quality frameworks exist, varying in complexity.
  • Existing frameworks often lack computational adaptability for diverse data sources.

Purpose of the Study:

  • To design, develop, and implement a platform-agnostic computable data quality knowledge repository.
  • To facilitate automated data quality assessments.

Main Methods:

  • Conducted a comprehensive literature review to identify computable data quality concepts.
  • Extracted concepts, definitions, measures, and relationships.
  • Developed a data quality meta-model and implemented a knowledge repository.

Main Results:

  • Identified three core primitives for programmatic data quality assessment: concept, definition, and measure.
  • Modeled a computable data quality meta-data repository.
  • Extended the framework for adaptability and automation of existing assessment models.

Conclusions:

  • Identified research gaps in automating data quality assessments.
  • Developed a computable knowledge repository for assessing and characterizing health data.
  • Leveraged the repository in a service-oriented architecture for scalable, reproducible assessments across disparate biomedical data sources.