School of Engineering, Okanagan Campus, The University of British Columbia, 3333 University Way, Kelowna, BC V1V 1V7, Canada.
Toxicology. 2013 Nov 16;313(2-3):160-73. doi: 10.1016/j.tox.2012.11.010. Epub 2012 Dec 3.
The exposure and toxicological data used in human health risk assessment are obtained from diverse and heterogeneous sources. Complex mixtures found on contaminated sites can pose a significant challenge to effectively assess the toxicity potential of the combined chemical exposure and to manage the associated risks. A data fusion framework has been proposed to integrate data from disparate sources to estimate potential risk for various public health issues. To demonstrate the effectiveness of the proposed data fusion framework, an illustrative example for a hydrocarbon mixture is presented. The Joint Directors of Laboratories Data Fusion architecture was selected as the data fusion architecture and Dempster-Shafer Theory (DST) was chosen as the technique for data fusion. For neurotoxicity response analysis, neurotoxic metabolites toxicological data were fused with predictive toxicological data and then probability-boxes (p-boxes) were developed to represent the toxicity of each compound. The neurotoxic response was given a rating of "low", "medium" or "high". These responses were then weighted by the percent composition in the illustrative F1 hydrocarbon mixture. The resulting p-boxes were fused according to DST's mixture rule of combination. The fused p-boxes were fused again with toxicity data for n-hexane. The case study for F1 hydrocarbons illustrates how data fusion can help in the assessment of the health effects for complex mixtures with limited available data.
在人类健康风险评估中使用的暴露和毒理学数据来自于不同且异构的来源。受污染地点发现的复杂混合物对有效评估化学暴露的综合毒性潜力以及管理相关风险构成了重大挑战。已经提出了一种数据融合框架，以整合来自不同来源的数据，以估计各种公共卫生问题的潜在风险。为了展示所提出的数据融合框架的有效性，提出了一个烃混合物的说明性示例。选择联合主任实验室数据融合架构作为数据融合架构，并选择 Dempster-Shafer 理论 (DST) 作为数据融合技术。对于神经毒性反应分析，融合了神经毒性代谢物毒理学数据和预测毒理学数据，然后开发了概率框 (p-box) 来表示每个化合物的毒性。将神经毒性反应评为“低”、“中”或“高”。然后，根据说明性 F1 碳氢化合物混合物中的百分组成对这些反应进行加权。根据 DST 的组合规则，对所得 p-box 进行融合。根据 DST 的组合规则，再次将融合的 p-box 与正己烷的毒性数据融合。F1 碳氢化合物的案例研究说明了数据融合如何有助于在有限可用数据的情况下评估复杂混合物对健康的影响。
