2015
A study of active learning methods for named entity recognition in clinical text
Chen Y, Lasko T, Mei Q, Denny J, Xu H. A study of active learning methods for named entity recognition in clinical text. Journal Of Biomedical Informatics 2015, 58: 11-18. PMID: 26385377, PMCID: PMC4934373, DOI: 10.1016/j.jbi.2015.09.010.Peer-Reviewed Original ResearchConceptsClinical NER tasksMachine learningAnnotation costF-measureEntity recognitionNER taskActive learningLearning methodsI2b2/VA NLP challengeNatural language processing systemsPerformance of MLClinical natural language processing (NLP) systemsSequential labeling tasksSupervised machine learningAL methodsLanguage processing systemDiversity-based methodReal-time settingActive learning methodsNew AL methodsNER corpusDomain expertsUncertainty samplingAnnotation effortClinical text
2013
Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
Tang B, Cao H, Wu Y, Jiang M, Xu H. Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features. BMC Medical Informatics And Decision Making 2013, 13: s1. PMID: 23566040, PMCID: PMC3618243, DOI: 10.1186/1472-6947-13-s1-s1.Peer-Reviewed Original ResearchConceptsStructural support vector machineWord representation featuresClinical NER tasksConditional Random FieldsSupport vector machinePerformance of MLClinical NER systemMachine learningRepresentation featuresNER systemNER taskVector machineEntity recognitionNatural language processing researchSequential labeling algorithmClinical entity recognitionLarge margin theoryClinical text processingLanguage processing researchPerformance of CRFsHighest F-measureClinical NLP researchI2b2 NLP challengeSame feature setsBetter performance