2024
Improving tabular data extraction in scanned laboratory reports using deep learning models
Li Y, Wei Q, Chen X, Li J, Tao C, Xu H. Improving tabular data extraction in scanned laboratory reports using deep learning models. Journal Of Biomedical Informatics 2024, 159: 104735. PMID: 39393477, DOI: 10.1016/j.jbi.2024.104735.Peer-Reviewed Original ResearchTree edit distanceOptical character recognitionTable recognitionDeep learning modelsAverage recallAverage precisionState-of-the-art deep learning modelsLearning modelsRegion-of-interest detectionState-of-the-artCharacter recognitionDetection evaluationTree editingTabular dataImpressive resultsLab test resultsLaboratory test reportsClinical documentationRecognitionLaboratory reportsHealthcare organizationsClinical data analysisDecision makingClinical decision makingTest reports
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 research
2018
Adapting Word Embeddings from Multiple Domains to Symptom Recognition from Psychiatric Notes.
Zhang Y, Li H, Wang J, Cohen T, Roberts K, Xu H. Adapting Word Embeddings from Multiple Domains to Symptom Recognition from Psychiatric Notes. AMIA Joint Summits On Translational Science Proceedings 2018, 2017: 281-289. PMID: 29888086, PMCID: PMC5961810.Peer-Reviewed Original ResearchWord embeddingsClinical textTarget domainSource domainNatural language processing techniquesLanguage processing techniquesMultiple word embeddingsBaseline methodsBiomedical literatureFirst workProcessing techniquesEmbeddingPsychiatric notesMultiple domainsExperimental resultsDifferent weightsSuch informationImportant topicRecognitionDifferent approachesWikipediaInformationPersonalizationDomainText
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 settingKnowledge-Based Approach for Named Entity Recognition in Biomedical Literature: A Use Case in Biomedical Software Identification
Amith M, Zhang Y, Xu H, Tao C. Knowledge-Based Approach for Named Entity Recognition in Biomedical Literature: A Use Case in Biomedical Software Identification. Lecture Notes In Computer Science 2017, 10351: 386-395. DOI: 10.1007/978-3-319-60045-1_40.Peer-Reviewed Original ResearchEntity recognitionNatural language processingContextual semantic informationNamed Entity RecognitionEntity recognition methodFeatures of ontologyMachine learning approachesKnowledge-based approachSoftware entitiesSoftware namesInformation extractionUse casesBiomedical softwareSemantic informationSoftware identificationLanguage processingRecognition methodLearning approachBiomedical literatureRecognitionOntologyEntitiesSoftwareResearch abstractsTask