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TREXMO plus: an advanced self-learning model for occupational exposure assessment.
Journal of Exposure Science and Environmental Epidemiology ( IF 4.1 ) Pub Date : 2020-02-03 , DOI: 10.1038/s41370-020-0203-9 Nenad Savic 1 , Eun Gyung Lee 2 , Bojan Gasic 3 , David Vernez 1
Journal of Exposure Science and Environmental Epidemiology ( IF 4.1 ) Pub Date : 2020-02-03 , DOI: 10.1038/s41370-020-0203-9 Nenad Savic 1 , Eun Gyung Lee 2 , Bojan Gasic 3 , David Vernez 1
Affiliation
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In Europe, several occupational exposure models have been developed and are recommended for regulatory exposure assessment. Only some information on the substance of interest (e.g., vapor pressure) and the workplace conditions (e.g., ventilation rate) is required in these models to predict an exposure value that will be later used to characterize the risk. However, it has been shown that models may differ in their predictions and that usually, one of the models best fits a given set of exposure conditions. Unfortunately, there are no clear rules on how to select the best model. In this study, we developed a new modeling approach that together uses the three most popular models, Advanced REACH Tool, Stoffenmanger, and ECETOC TRAv3, to obtain a unique exposure prediction. This approach is an extension of the TREXMO tool, and is called TREXMO+. TREXMO+ applies a machine-learning technique on a set of exposure data with the measured values to split them into smaller subsets, corresponding to exposure conditions sharing similar characteristics. For each subset, TREXMO+ then establishes a regression model with the three REACH tools used as the exposure predictors. The performance of the new model was tested and a comparison was made between the results obtained by TREXMO+ and those obtained by conventional tools. TREXMO+ model was found to be less biased and more accurate than the REACH models. Its prediction differs generally from measurements by a factor of 2-3 from measurements, whereas conventional models were found to differ by a factor 2-14. However, as the available test dataset is limited, its results will need to be confirmed by larger-scale tests.
中文翻译：
TREXMO plus：职业暴露评估的先进自学模型。
在欧洲，已经开发了几种职业暴露模型并推荐用于监管暴露评估。这些模型只需要有关感兴趣物质（例如蒸气压）和工作场所条件（例如通风率）的一些信息来预测稍后将用于表征风险的暴露值。然而，事实证明，模型的预测可能有所不同，并且通常，其中一个模型最适合一组给定的暴露条件。不幸的是，对于如何选择最佳模型没有明确的规则。在本研究中，我们开发了一种新的建模方法，结合使用三种最流行的模型：Advanced REACH Tool、Stoffenmanger 和 ECETOC TRAv3，以获得独特的暴露预测。这种方法是 TREXMO 工具的扩展，称为 TREXMO+。 TREXMO+ 对一组曝光数据和测量值应用机器学习技术，将它们分成更小的子集，对应于具有相似特征的曝光条件。对于每个子集，TREXMO+ 然后使用三个 REACH 工具作为暴露预测因子建立回归模型。对新模型的性能进行了测试，并将TREXMO+获得的结果与传统工具获得的结果进行了比较。研究发现 TREXMO+ 模型比 REACH 模型偏差更小且更准确。它的预测通常与测量结果相差 2-3 倍，而传统模型则与测量结果相差 2-14 倍。但由于可用的测试数据集有限，其结果需要通过更大规模的测试来证实。
更新日期：2020-02-03
中文翻译：
TREXMO plus：职业暴露评估的先进自学模型。
在欧洲，已经开发了几种职业暴露模型并推荐用于监管暴露评估。这些模型只需要有关感兴趣物质（例如蒸气压）和工作场所条件（例如通风率）的一些信息来预测稍后将用于表征风险的暴露值。然而，事实证明，模型的预测可能有所不同，并且通常，其中一个模型最适合一组给定的暴露条件。不幸的是，对于如何选择最佳模型没有明确的规则。在本研究中，我们开发了一种新的建模方法，结合使用三种最流行的模型：Advanced REACH Tool、Stoffenmanger 和 ECETOC TRAv3，以获得独特的暴露预测。这种方法是 TREXMO 工具的扩展，称为 TREXMO+。 TREXMO+ 对一组曝光数据和测量值应用机器学习技术，将它们分成更小的子集，对应于具有相似特征的曝光条件。对于每个子集，TREXMO+ 然后使用三个 REACH 工具作为暴露预测因子建立回归模型。对新模型的性能进行了测试，并将TREXMO+获得的结果与传统工具获得的结果进行了比较。研究发现 TREXMO+ 模型比 REACH 模型偏差更小且更准确。它的预测通常与测量结果相差 2-3 倍，而传统模型则与测量结果相差 2-14 倍。但由于可用的测试数据集有限，其结果需要通过更大规模的测试来证实。
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