2020
Named Entity Recognition from Table Headers in Randomized Controlled Trial Articles
Wei Q, Zhou Y, Zhao B, Hu X, Mei Q, Tao C, Xu H. Named Entity Recognition from Table Headers in Randomized Controlled Trial Articles. 2020, 00: 1-2. DOI: 10.1109/ichi48887.2020.9374323.Peer-Reviewed Original ResearchTable headersEntity recognitionDeep learning-based approachBiomedical text miningLearning-based approachNamed Entity RecognitionInformation extractionBiomedical entitiesF1 scoreText miningUnstructured natureBiomedical articlesContextual informationComputational applicationsHeaderSemantic complexityBetter performanceCorpusRecognitionInformationMiningApplicationsImportant informationComplexityBiomedical researchEfficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus
Li Y, Luo Y, Wampfler J, Rubinstein S, Tiryaki F, Ashok K, Warner J, Xu H, Yang P. Efficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus. JCO Clinical Cancer Informatics 2020, 4: cci.19.00147. PMID: 32364754, PMCID: PMC7265793, DOI: 10.1200/cci.19.00147.Peer-Reviewed Original ResearchConceptsNatural language processing toolsElectronic health recordsLanguage processing toolsGold standard dataUnstructured electronic health recordsProcessing toolsAmount of dataClinical notesStandard dataMayo Clinic electronic health recordsClinic's electronic health recordEnvironment toolsAccurate annotationHealth recordsInformatics toolsEffective analysisData setsTextual sourcesCorpusToolInformationData extractionSetExtractingAnnotation
2018
Clinical text annotation - what factors are associated with the cost of time?
Wei Q, Franklin A, Cohen T, Xu H. Clinical text annotation - what factors are associated with the cost of time? AMIA Annual Symposium Proceedings 2018, 2018: 1552-1560. PMID: 30815201, PMCID: PMC6371268.Peer-Reviewed Original ResearchConceptsAnnotation timeClinical textNatural language processing modelsClinical corpusIndividual user behaviorEntity recognition taskLanguage processing modelsPractice of annotationCharacteristics of sentencesClinical Text AnnotationText annotationsUser behaviorIndividual usersCost of timeActive learning researchRecognition taskLearning researchProcessing modelCost modelAnnotationUsersLimited workCorpusTextTask
2017
Information retrieval for biomedical datasets: the 2016 bioCADDIE dataset retrieval challenge
Roberts K, Gururaj A, Chen X, Pournejati S, Hersh W, Demner-Fushman D, Ohno-Machado L, Cohen T, Xu H. Information retrieval for biomedical datasets: the 2016 bioCADDIE dataset retrieval challenge. Database 2017, 2017: bax068. DOI: 10.1093/database/bax068.Peer-Reviewed Original ResearchBiomedical datasetsRetrieval challengesInformation retrieval techniquesAdvanced query processingBiomedical data repositoriesAdvanced retrieval methodsQuery processingInformation retrievalTest queriesRetrieval systemRank frameworkRetrieval approachRetrieval techniquesData repositoryRetrieval methodTop precisionDatasetQueriesRepositoryChallengesRetrievalTaskLearningSystemCorpus
2015
Domain adaptation for semantic role labeling of clinical text
Zhang Y, Tang B, Jiang M, Wang J, Xu H. Domain adaptation for semantic role labeling of clinical text. Journal Of The American Medical Informatics Association 2015, 22: 967-979. PMID: 26063745, PMCID: PMC4986662, DOI: 10.1093/jamia/ocu048.Peer-Reviewed Original Research