Prioritization of near-miss incidents using text mining and Bayesian network

Abstract

Near-Miss incidents can be treated as events to signal the weakness of safety management system (SMS) at the workplace. Analyzing near-misses will provide relevant root causes behind such incidents so that effective safety related interventions can be developed beforehand. Despite having a huge potential towards workplace safety improvements, analysis of near-misses is scant in the literature owing to the fact that near-misses are often reported as text narratives. The aim of this study is therefore to explore text-mining for extraction of root causes of near-misses from the narrative text descriptions of such incidents and to measure their relationships probabilistically. Root causes were extracted by word cloud technique and causal model was constructed using a Bayesian network (BN). Finally, using BN’s inference mechanism, scenarios were evaluated and root causes were listed in a prioritized order. A case study in a steel plant validated the approach and raised concerns for variety of circumstances such as incidents related to collision, slip-trip-fall, and working at height.