Department of Accounting and Finance, University of Limerick, V94PH93 Limerick, Ireland.
Institute of Science and Technology for Ceramics (CNR-ISTEC), National Research Council of Italy, Via Granarolo 64, 48018 Faenza (RA), Italy.
Int J Mol Sci. 2018 Feb 25;19(3):649. doi: 10.3390/ijms19030649.
Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitative WoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs-TiO₂, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN and WoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework.
危害识别是制造纳米材料（NM）风险评估和管理的关键步骤。然而，纳米增强产品的快速商业化继续超过了广泛为科学界所接受和监管机构所执行的谨慎风险管理机制的发展。然而，越来越多的学术文献正在开发有前途的定量方法。两种方法已经得到了广泛的应用。贝叶斯网络（BN）是一种概率机器学习方法，而证据权重（WoE）统计框架则基于专家判断。本研究调查了定量 WoE 和贝叶斯方法在对金属和金属氧化物 NM-TiO₂、Ag 和 ZnO 的潜在危害进行排序方面的效果。这项研究发现，这两种风险评估方法的危害排序是一致的。BN 和 WoE 模型都利用物理化学、毒理学和研究类型的数据来推断危害潜力。当模型受到新数据的干扰时，BN 表现出更高的稳定性。BN 具有利用新数据进行自我学习的显著优势；但是，这假设所有输入数据都是同样有效的。本研究发现，WoE 与 BN 相结合的方法是最佳的危害评估框架。
