Advancing occupational exposure models: insights from a case study.

Currently used models in occupational exposure assessment are mostly mechanistic-based and their performance varies. The usage of more advanced modeling approaches such as machine learning (ML) or hybrid modeling approaches such as Bayesian Networks could improve accuracy of exposure predictions. Within this study, a case study was conducted where 5 different ML or hybrid modeling approaches were used to redevelop the "asbestos removal exposure assessment tool," an existing mechanistic exposure model, and their performance was compared. Multiple Linear Regression, Random Forest, Gradient Boosting Machines, Bayesian Network, and Neural Network models were developed using the same dataset and model determinants used to develop the mechanistic model. Random Forest and Gradient Boosting Machines performed best with regards to accuracy, followed by Bayesian Network, Multiple Linear Regression, the original model, and Neural Network. Such models show a promise for the development of more accurate models, but their limitations need to be considered before they can be implemented in a regulatory context. For these models, a trade-off exists where transparency in decision-making is traded against accuracy. Hybrid models such as Bayesian Networks might be a solution for this trade-off as expert knowledge and data-driven approaches are combined in a transparent model.