A New Text-Mining–Bayesian Network Approach for Identifying Chemical Safety Risk Factors

This paper combines text mining with Bayesian network modeling to identify and relate chemical safety risk factors from accident-report text. After extracting factors from unstructured narratives, Bayesian networks represent dependencies and enable probabilistic inference about how factors interact. For PSM incident investigation, the approach helps move from ad hoc narrative review toward repeatable, data-supported causal modeling—supporting systemic root-cause identification, sensitivity analysis, and prioritization of corrective actions.