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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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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Automated Tools for Clinical Research Data Quality Control using NCI Common Data Elements.

Cody L Hudson1, Umit Topaloglu1, Jiang Bian1

  • 1University of Arkansas for Medical Sciences, Little Rock, AR.

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|February 27, 2015
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Summary
This summary is machine-generated.

Two new tools enhance clinical research data quality by automatically suggesting National Cancer Institute Common Data Elements (CDEs) and constraining new questions. This improves data consistency and reduces study costs.

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Area of Science:

  • Biomedical Informatics
  • Clinical Research Data Management

Background:

  • Clinical research data often lacks standardization, leading to quality issues and inefficient resource utilization.
  • Poor data quality can necessitate costly study repetitions, impacting research timelines and budgets.

Purpose of the Study:

  • To present two novel tools designed to improve the data quality of clinical research.
  • To leverage the National Cancer Institute's Common Data Elements (CDEs) as a standardized framework for data annotation and constraint.

Main Methods:

  • Development of a tool for automatic CDE suggestion using semantic and syntactic analysis of existing data via the Unified Medical Language System (UMLS) Metathesaurus.
  • Creation of a user-friendly 'CDE Browser' for annotating and constraining new clinical research questions.
  • Implementation and testing of these tools on the open-source LimeSurvey platform using data from the Comprehensive Research Informatics Suite (CRIS).

Main Results:

  • The developed tools demonstrated the capability to automatically suggest CDE annotations for collected data.
  • The CDE Browser effectively facilitated the annotation and constraint of new research questions.
  • Testing on real-world data identified and addressed various data quality issues.

Conclusions:

  • The presented tools offer a viable solution for enhancing clinical research data quality.
  • Standardized data elements and improved annotation processes can lead to more efficient and reliable research.
  • These tools have the potential to reduce costs and improve the overall integrity of clinical research data.