scholarly article | Q13442814 |
P50 | author | Alysha R. Meyers | Q37380075 |
Steven J. Wurzelbacher | Q85315547 | ||
Stephen J. Bertke | Q88326813 | ||
Jennifer L. Bell | Q124638462 | ||
P2093 | author name string | D Robins | |
M L Lampl | |||
P2860 | cites work | Computerized 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 narratives | Q37863940 | ||
Bayesian methods: a useful tool for classifying injury narratives into cause groups | Q49103417 | ||
Machine learning in automated text categorization | Q57771154 | ||
A THEORETICAL BASIS FOR THE USE OF CO‐OCCURRENCE DATA IN INFORMATION RETRIEVAL | Q58632222 | ||
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss | Q59699931 | ||
P433 | issue | 5-6 | |
P407 | language of work or name | English | Q1860 |
P921 | main subject | disease | Q12136 |
workplace | Q628858 | ||
ergonomics | Q1750812 | ||
accident analysis | Q2493120 | ||
musculoskeletal disorder | Q4116663 | ||
workplace health surveillance | Q8035062 | ||
public security | Q294240 | ||
P5008 | on focus list of Wikimedia project | Wikimedia–NIOSH collaboration | Q104416361 |
P304 | page(s) | 327-32 | |
P577 | publication date | 2012-12-01 | |
P1433 | published in | Journal of Safety Research | Q15751998 |
P859 | sponsor | NIOSH Division of Field Studies and Engineering | Q123344454 |
NIOSH Division of Safety Research | Q123344456 | ||
P1476 | title | Development and evaluation of a Naïve Bayesian model for coding causation of workers' compensation claims | |
P478 | volume | 43 |
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Q45068512 | Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011. |
Q23920770 | Comparison of methods for auto-coding causation of injury narratives |
Q28394640 | Computerized "Learn-As-You-Go" classification of traumatic brain injuries using NEISS narrative data |
Q27908441 | Development of methods for using workers' compensation data for surveillance and prevention of occupational injuries among State-insured private employers in Ohio |
Q28393453 | Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance |
Q28396009 | Injury narrative text classification using factorization model |
Q23914648 | Near-miss narratives from the fire service: a Bayesian analysis |
Q23917011 | Occupational injuries in Ohio wood product manufacturing: a descriptive analysis with emphasis on saw-related injuries and associated causes |
Q51140794 | The 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. |
Q28950590 | Trends in non-fatal agricultural injury in Maine and New Hampshire: results from a low-cost passive surveillance system |
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