Moayed Farman A, Shell Richard L
Department of the Built Environment, Indiana State University, Terre Haute, IN 47809, USA.
Ann Occup Hyg. 2011 Mar;55(2):143-51. doi: 10.1093/annhyg/meq080. Epub 2010 Dec 14.
Statistical analysis and logistic regression (LR) in particular are among the most popular tools being used by safety professionals and practitioners to assess the association between exposures and possible occupational disorders or diseases and predict the outcome. Recently, artificial neural network (ANN) models are gradually finding their way into safety field. It has been shown that they are capable of predicting outcomes more accurately than LR, but they are incapable of demonstrating the direct correlation between exposure variables and a possible outcome variable. The objective of this study was to develop a mathematical function that can use the result of ANN models to produce a measure for evaluating the direct association between exposure and possible outcome variables. This function was referred to as the function of Magnitude-of-Effect (MoE). Safety experts and practitioners can use the MoE function to interpret how strongly an exposure variable can affect the outcome variable, similar to an odds ratio, which can be calculated by using estimated parameters in LR models. The significance of such achievement is that it can eliminate one of the ANN model's shortcoming and make them more applicable in the occupational safety and health engineering field.
统计分析，尤其是逻辑回归（LR），是安全专业人员和从业者用于评估暴露与可能的职业性疾病或病症之间的关联并预测结果的最常用工具之一。最近，人工神经网络（ANN）模型正逐渐进入安全领域。研究表明，它们能够比LR更准确地预测结果，但无法证明暴露变量与可能的结果变量之间的直接相关性。本研究的目的是开发一种数学函数，该函数可以利用ANN模型的结果来生成一种用于评估暴露与可能的结果变量之间直接关联的度量。该函数被称为效应量（MoE）函数。安全专家和从业者可以使用MoE函数来解释暴露变量对结果变量的影响程度，类似于优势比，优势比可以通过使用LR模型中的估计参数来计算。这一成果的意义在于它可以消除ANN模型的一个缺点，使其在职业安全与健康工程领域更具适用性。
