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http://dx.doi.org/10.13103/JFHS.2020.35.6.607

Knowledge Modeling and Database Construction for Human Biomonitoring Data  

Lee, Jangwoo (Department of Integrated Risk Assessment Research, Chem.I.Net Ltd.)
Yang, Sehee (Department of Integrated Risk Assessment Research, Chem.I.Net Ltd.)
Lee, Hunjoo (Department of Integrated Risk Assessment Research, Chem.I.Net Ltd.)
Publication Information
Journal of Food Hygiene and Safety / v.35, no.6, 2020 , pp. 607-617 More about this Journal
Abstract
Human bio-monitoring (HBM) data is a very important resource for tracking total exposure and concentrations of a parent chemical or its metabolites in human biomarkers. However, until now, it was difficult to execute the integration of different types of HBM data due to incompatibility problems caused by gaps in study design, chemical description and coding system between different sources in Korea. In this study, we presented a standardized code system and HBM knowledge model (KM) based on relational database modeling methodology. For this purpose, we used 11 raw datasets collected from the Ministry of Food and Drug Safety (MFDS) between 2006 and 2018. We then constructed the HBM database (DB) using a total of 205,491 concentration-related data points for 18,870 participants and 86 chemicals. In addition, we developed a summary report-type statistical analysis program to verify the inputted HBM datasets. This study will contribute to promoting the sustainable creation and versatile utilization of big-data for HBM results at the MFDS.
Keywords
Human biomonitoring; Knowledge model; Database; Statistical analysis;
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