Publication

Biomedical heterogeneous data categorization and schema mapping toward data integration.

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Last modified
  • 05/22/2025
Type of Material
Authors
    Priya Deshpande, Marquette UniversityAlexander Rasin, DePaul UniversityRoselyne Tchoua, DePaul UniversityJacob Furst, DePaul UniversityDaniela Raicu, DePaul UniversityMichiel Schinkel, University of AmsterdamHari Trivedi, Emory UniversitySameer Antani, National Institutes of Health
Language
  • English
Date
  • 2023
Publisher
  • Frontiers
Publication Version
Copyright Statement
  • © 2023 Deshpande, Rasin, Tchoua, Furst, Raicu, Schinkel, Trivedi and Antani.
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 6
Start Page
  • 1173038
End Page
  • 1173038
Grant/Funding Information
  • This research was supported in part by the Intramural Research Program of the National Institutes of Health (NIH), National Library of Medicine (NLM), and Lister Hill National Center for Biomedical Communications (LHNCBC).
Abstract
  • Data integration is a well-motivated problem in the clinical data science domain. Availability of patient data, reference clinical cases, and datasets for research have the potential to advance the healthcare industry. However, the unstructured (text, audio, or video data) and heterogeneous nature of the data, the variety of data standards and formats, and patient privacy constraint make data interoperability and integration a challenge. The clinical text is further categorized into different semantic groups and may be stored in different files and formats. Even the same organization may store cases in different data structures, making data integration more challenging. With such inherent complexity, domain experts and domain knowledge are often necessary to perform data integration. However, expert human labor is time and cost prohibitive. To overcome the variability in the structure, format, and content of the different data sources, we map the text into common categories and compute similarity within those. In this paper, we present a method to categorize and merge clinical data by considering the underlying semantics behind the cases and use reference information about the cases to perform data integration. Evaluation shows that we were able to merge 88% of clinical data from five different sources.
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Research Categories
  • Artificial Intelligence

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