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Identification and prioritization of relationships between environmental stressor and adverse human health impacts
Citation:
Bell, S. AND S. Edwards. Identification and prioritization of relationships between environmental stressor and adverse human health impacts. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, 123(11):1193-1199, (2015).
Impact/Purpose:
The framework and resulting prioritized list is a resource for evaluating results from targeted data mining and putting them into the larger context of the dataset, identifying novel candidate relationships for further testing, and identifying potential increased risks of adverse health outcomes due to coexposure
Description:
AbstractBackground: There are over 80,000 chemicals in commerce with little data available describing their impacts on human health. Biomonitoring surveys, such as the NHANES, offer one route to identifying possible relationships between environmental chemicals and health impacts, but sparse data and the complexity of traditional models makes it difficult to leverage effectively.Objective: We describe a workflow to efficiently and comprehensively evaluate and prioritize chemical-health impact relationships from the NHANES biomonitoring survey studies. Methods: Using a frequent itemset mining (FIM) approach, chemical to health biomarker and disease relationships were identified. Results: The FIM method identified 4,170 relationships between 220 chemicals and 66 health outcomes/ biomarkers. Two case studies used to evaluate the FIM rankings demonstrate that the FIM approach is able to identify published relationships. Since the relationships are derived from the vast majority of the chemicals monitored by NHANES, the resulting list of associations is appropriate for evaluating results from targeted data mining or identifying novel candidate relationships for more detailed investigation. Conclusions: The FIM approach enables ranking and prioritization on chemicals or health effects of interest, allowing the identification of most likely co-occurring relationships. Due to the computational efficiency of this method, all chemicals and health effects can be considered in a single analysis. The resulting list provides comprehensive information about the relative likelihood of any chemical/health association including those previously published in the literature.
