Characterizing accident narratives with word embeddings: Improving accuracy, richness, and generalizability.
Journal:
Journal of safety research
PMID:
35249625
Abstract
INTRODUCTION: Ensuring occupational health and safety is an enormous concern for organizations, as accidents not only harm workers but also result in financial losses. Analysis of accident data has the potential to reveal insights that may improve capabilities to mitigate future accidents. However, because accident data are often transcribed textually, analyzing these narratives proves difficult. This study contributes to a recent stream of literature utilizing machine learning to automatically label accident narratives, converting them into more easily analyzable fields.