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Applying active learning to assertion classification of concepts in clinical text
2012
Journal of Biomedical Informatics
Supervised machine learning methods for clinical natural language processing (NLP) research require a large number of annotated samples, which are very expensive to build because of the involvement of physicians. Active learning, an approach that actively samples from a large pool, provides an alternative solution. Its major goal in classification is to reduce the annotation effort while maintaining the quality of the predictive model. However, few studies have investigated its uses in clinical
doi:10.1016/j.jbi.2011.11.003
pmid:22127105
pmcid:PMC3306548
fatcat:kyyvw5b5ufectmsrc4yisyd5e4