Cost-aware active learning for named entity recognition in clinical text
Wei Q, Chen Y, Salimi M, Denny J, Mei Q, Lasko T, Chen Q, Wu S, Franklin A, Cohen T, Xu H. Cost-aware active learning for named entity recognition in clinical text. Journal Of The American Medical Informatics Association 2019, 26: 1314-1322. PMID: 31294792, PMCID: PMC6798575, DOI: 10.1093/jamia/ocz102.Peer-Reviewed Original ResearchConceptsAnnotation costUser studyActive learningAL methodsAL algorithmCost-CAUSEReal-world environmentsAnnotation taskAnnotation timeAnnotation accuracyEntity recognitionClinical textAnnotation dataPassive learningInformative examplesCurve scoreMost approachesSimulation areaUsersSyntactic featuresLearningCost measuresAlgorithmCostAnnotationEnhancing clinical concept extraction with contextual embeddings
Si Y, Wang J, Xu H, Roberts K. Enhancing clinical concept extraction with contextual embeddings. Journal Of The American Medical Informatics Association 2019, 26: 1297-1304. PMID: 31265066, PMCID: PMC6798561, DOI: 10.1093/jamia/ocz096.Peer-Reviewed Original ResearchConceptsClinical concept extractionContextual embeddingsNatural language processing tasksTraditional word embeddingsTraditional word representationsClinical NLP tasksLanguage processing tasksSemantic informationWord embedding methodsLarge language modelsArt performanceConcept extraction taskSemEval 2014Word representationsNLP tasksLanguage modelWord embeddingsProcessing tasksNeural network-based representationI2b2 2010Concept extractionTaskLarge clinical corpusClinical corpusNetwork-based representation