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An exploration of text mining of narrative reports of injury incidents to assess risk
In this article, a topic model was explored using unsupervised machine learning to summarized free-text narrative reports of 77,215 injuries that occurred in coal mines in the USA between 2000 and 2015. Latent Dirichlet Allocation modeling processes identified six topics from the free-text data. The modeling success enjoyed in this exploratory effort suggests that additional topic mining of these injury text narratives is justified, especially using a broad set of covariates to explain variations in topic emphasis and for comparison of surface mining injuries with injuries occurring during site preparation for construction.
Date:
December 14, 2018
Creator:
Passmore, David L.; Chae, Chungil; Kustikova, Yulia; Baker, Rose M. & Yim, Jeong-Ha
System:
The UNT Digital Library