Comparison of methods for auto-coding causation of injury narratives
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Development of methods for using workers' compensation data for surveillance and prevention of occupational injuries among State-insured private employers in OhioTrends in non-fatal agricultural injury in Maine and New Hampshire: results from a low-cost passive surveillance systemApplying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011.
P2860
Comparison of methods for auto-coding causation of injury narratives
description
2016 nî lūn-bûn
@nan
2016 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2016 թվականի մարտին հրատարակված գիտական հոդված
@hy
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
name
Comparison of methods for auto-coding causation of injury narratives
@ast
Comparison of methods for auto-coding causation of injury narratives
@en
Comparison of methods for auto-coding causation of injury narratives
@nl
type
label
Comparison of methods for auto-coding causation of injury narratives
@ast
Comparison of methods for auto-coding causation of injury narratives
@en
Comparison of methods for auto-coding causation of injury narratives
@nl
prefLabel
Comparison of methods for auto-coding causation of injury narratives
@ast
Comparison of methods for auto-coding causation of injury narratives
@en
Comparison of methods for auto-coding causation of injury narratives
@nl
P2093
P2860
P921
P1476
Comparison of methods for auto-coding causation of injury narratives
@en
P2093
S J Bertke
S J Wurzelbacher
P2860
P2880
P304
P356
10.1016/J.AAP.2015.12.006
P407
P577
2016-03-01T00:00:00Z