Development and evaluation of a Naïve Bayesian model for coding causation of workers' compensation claims

journal article published in 2012

Development and evaluation of a Naïve Bayesian model for coding causation of workers' compensation claims is …
instance of (P31):
scholarly articleQ13442814

External links are
P356DOI10.1016/J.JSR.2012.10.012
P2880NIOSHTIC-2 ID20041772
P932PMC publication ID4550086
P698PubMed publication ID23206504

P50authorAlysha R. MeyersQ37380075
Steven J. WurzelbacherQ85315547
Stephen J. BertkeQ88326813
Jennifer L. BellQ124638462
P2093author name stringD Robins
M L Lampl
P2860cites workComputerized coding of injury narrative data from the National Health Interview Survey.Q30884909
A combined Fuzzy and Naive Bayesian strategy can be used to assign event codes to injury narrativesQ37863940
Bayesian methods: a useful tool for classifying injury narratives into cause groupsQ49103417
Machine learning in automated text categorizationQ57771154
A THEORETICAL BASIS FOR THE USE OF CO‐OCCURRENCE DATA IN INFORMATION RETRIEVALQ58632222
On the Optimality of the Simple Bayesian Classifier under Zero-One LossQ59699931
P433issue5-6
P407language of work or nameEnglishQ1860
P921main subjectdiseaseQ12136
workplaceQ628858
ergonomicsQ1750812
accident analysisQ2493120
musculoskeletal disorderQ4116663
workplace health surveillanceQ8035062
public securityQ294240
P5008on focus list of Wikimedia projectWikimedia–NIOSH collaborationQ104416361
P304page(s)327-32
P577publication date2012-12-01
P1433published inJournal of Safety ResearchQ15751998
P859sponsorNIOSH Division of Field Studies and EngineeringQ123344454
NIOSH Division of Safety ResearchQ123344456
P1476titleDevelopment and evaluation of a Naïve Bayesian model for coding causation of workers' compensation claims
P478volume43

Reverse relations

cites work (P2860)
Q26341209Analysis of workers' compensation claims data for machine-related injuries in metal fabrication businesses
Q45068512Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011.
Q23920770Comparison of methods for auto-coding causation of injury narratives
Q28394640Computerized "Learn-As-You-Go" classification of traumatic brain injuries using NEISS narrative data
Q27908441Development of methods for using workers' compensation data for surveillance and prevention of occupational injuries among State-insured private employers in Ohio
Q28393453Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance
Q28396009Injury narrative text classification using factorization model
Q23914648Near-miss narratives from the fire service: a Bayesian analysis
Q23917011Occupational injuries in Ohio wood product manufacturing: a descriptive analysis with emphasis on saw-related injuries and associated causes
Q51140794The association of the original OSHA chemical hazard communication standard with reductions in acute work injuries/illnesses in private industry and the industrial releases of chemical carcinogens.
Q28950590Trends in non-fatal agricultural injury in Maine and New Hampshire: results from a low-cost passive surveillance system
Q38598054Wearable Devices for Classification of Inadequate Posture at Work Using Neural Networks
Q41659174Workplace slip, trip and fall injuries and obesity

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