2019
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 measuresAlgorithmCostAnnotationCost-sensitive Active Learning for Phenotyping of Electronic Health Records.
Ji Z, Wei Q, Franklin A, Cohen T, Xu H. Cost-sensitive Active Learning for Phenotyping of Electronic Health Records. AMIA Joint Summits On Translational Science Proceedings 2019, 2019: 829-838. PMID: 31259040, PMCID: PMC6568101.Peer-Reviewed Original ResearchAnnotation timeElectronic health recordsActive learningMachine learning-based methodsCost-sensitive active learningLarge annotated datasetLearning-based methodsHealth recordsUse casesAnnotated datasetUser 1AL algorithmUser 2Phenotyping algorithmAL approachSecondary useAlgorithmBetter performanceActual timeLearningExperimental resultsBreast cancer patientsDatasetModel performancePassive learning
2017
An active learning-enabled annotation system for clinical named entity recognition
Chen Y, Lask T, Mei Q, Chen Q, Moon S, Wang J, Nguyen K, Dawodu T, Cohen T, Denny J, Xu H. An active learning-enabled annotation system for clinical named entity recognition. BMC Medical Informatics And Decision Making 2017, 17: 82. PMID: 28699546, PMCID: PMC5506567, DOI: 10.1186/s12911-017-0466-9.Peer-Reviewed Original ResearchConceptsNovel AL algorithmAL algorithmAnnotation timeUser studyEntity recognitionAnnotation systemNatural language processing modelsLanguage processing modelsAnnotation costMedical domainAnnotation processDifferent usersNER modelProcessing modelAlgorithmAL methodsResultsThe simulation resultsUsersSimulation resultsInformation contentFuture workRecognitionLarge numberSystemReal-life setting