An exploration of text mining of narrative reports of injury incidents to assess risk

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. One topic, a theme d...

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Main Authors:	Passmore David, Chae Chungil, Kustikova Yulia, Baker Rose, Yim Jeong-Ha
Format:	Article
Language:	English
Published:	EDP Sciences 2018-01-01
Series:	MATEC Web of Conferences
Online Access:	https://doi.org/10.1051/matecconf/201825106020

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spelling	doaj-8553ed17432342e2aeedfc77c81019992021-02-02T00:20:25ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-012510602010.1051/matecconf/201825106020matecconf_ipicse2018_06020An exploration of text mining of narrative reports of injury incidents to assess riskPassmore David0Chae Chungil1Kustikova Yulia2Baker Rose3Yim Jeong-Ha4Penn State University, Workforce Education and DevelopmentPenn State University, Applied Cognitive Science LabFederal State Educational Institution of Higher Education, National Research Moscow State University of Civil EngineeringUniversity of North Texas, Learning TechnologiesUniversity of Georgia, Lifelong Education, Administration, and PolicyA 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. One topic, a theme describing primarily injury incidents resulting in strains and sprains of musculoskeletal systems, revealed differences in topic emphasis by the location of the mine property at which injuries occurred, the degree of injury, and the year of injury occurrence. Text narratives clustered around this topic refer most frequently to surface or other locations rather than underground locations that resulted in disability and that, also, increased secularly over time. 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.https://doi.org/10.1051/matecconf/201825106020
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Passmore David Chae Chungil Kustikova Yulia Baker Rose Yim Jeong-Ha
spellingShingle	Passmore David Chae Chungil Kustikova Yulia Baker Rose Yim Jeong-Ha An exploration of text mining of narrative reports of injury incidents to assess risk MATEC Web of Conferences
author_facet	Passmore David Chae Chungil Kustikova Yulia Baker Rose Yim Jeong-Ha
author_sort	Passmore David
title	An exploration of text mining of narrative reports of injury incidents to assess risk
title_short	An exploration of text mining of narrative reports of injury incidents to assess risk
title_full	An exploration of text mining of narrative reports of injury incidents to assess risk
title_fullStr	An exploration of text mining of narrative reports of injury incidents to assess risk
title_full_unstemmed	An exploration of text mining of narrative reports of injury incidents to assess risk
title_sort	exploration of text mining of narrative reports of injury incidents to assess risk
publisher	EDP Sciences
series	MATEC Web of Conferences
issn	2261-236X
publishDate	2018-01-01
description	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. One topic, a theme describing primarily injury incidents resulting in strains and sprains of musculoskeletal systems, revealed differences in topic emphasis by the location of the mine property at which injuries occurred, the degree of injury, and the year of injury occurrence. Text narratives clustered around this topic refer most frequently to surface or other locations rather than underground locations that resulted in disability and that, also, increased secularly over time. 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.
url	https://doi.org/10.1051/matecconf/201825106020
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An exploration of text mining of narrative reports of injury incidents to assess risk

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