2024
Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition
Zuo X, Kumar A, Shen S, Li J, Cong G, Jin E, Chen Q, Warner J, Yang P, Xu H. Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition. JCO Clinical Cancer Informatics 2024, 8: e2300166. PMID: 38885475, DOI: 10.1200/cci.23.00166.Peer-Reviewed Original ResearchConceptsNatural language processingDomain-specific language modelsNatural language processing systemsInformation extraction systemRule-based moduleNarrative clinical textsNLP tasksEntity recognitionText normalizationAssertion classificationLanguage modelInformation extractionClinical textElectronic health recordsLearning-basedClinical notesLanguage processingTest setSystem performanceHealth recordsResponse extractionTime-consumingAnticancer therapyInformationAssessment information
2021
From Tokenization to Self-Supervision: Building a High-Performance Information Extraction System for Chemical Reactions in Patents
Wang J, Ren Y, Zhang Z, Xu H, Zhang Y. From Tokenization to Self-Supervision: Building a High-Performance Information Extraction System for Chemical Reactions in Patents. Frontiers In Research Metrics And Analytics 2021, 6: 691105. PMID: 35005421, PMCID: PMC8727901, DOI: 10.3389/frma.2021.691105.Peer-Reviewed Original ResearchEvent extractionEntity recognitionNatural language processing techniquesAccurate information extractionInformation extraction systemLanguage processing techniquesKnowledge-based rulesInformation extractionAutomatic toolEnd systemArt resultsSemantic rolesLanguage modelSelf-SupervisionFree textChemical patentsSubtask 1Reaction extractionDifferent semantic rolesHybrid approachEvent triggersProcessing techniquesSubtasksTokenizationHigh performance
2016
Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning
Zhang Y, Xu J, Chen H, Wang J, Wu Y, Prakasam M, Xu H. Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning. Database 2016, 2016: baw049. PMID: 27087307, PMCID: PMC4834204, DOI: 10.1093/database/baw049.Peer-Reviewed Original ResearchConceptsMachine learning-based systemsLearning-based systemConditional Random FieldsDomain knowledgeEntity recognitionMatthews correlation coefficientDrug Named Entity RecognitionBioCreative V challengeInformation extraction systemWord representation featuresUnsupervised feature learningUnsupervised learning algorithmNamed Entity RecognitionSemantic type informationSupport vector machinePrecision-recall curveBrown clusteringFeature learningFeature engineeringUnsupervised featureIndividual subtasksMining systemNER taskLearning algorithmCPD task
2014
Extracting and standardizing medication information in clinical text - the MedEx-UIMA system.
Jiang M, Wu Y, Shah A, Priyanka P, Denny J, Xu H. Extracting and standardizing medication information in clinical text - the MedEx-UIMA system. AMIA Joint Summits On Translational Science Proceedings 2014, 2014: 37-42. PMID: 25954575, PMCID: PMC4419757.Peer-Reviewed Original Research
2010
MedEx: a medication information extraction system for clinical narratives
Xu H, Stenner S, Doan S, Johnson K, Waitman L, Denny J. MedEx: a medication information extraction system for clinical narratives. Journal Of The American Medical Informatics Association 2010, 17: 19-24. PMID: 20064797, PMCID: PMC2995636, DOI: 10.1197/jamia.m3378.Peer-Reviewed Original ResearchConceptsClinic visit notesVisit notesMedication informationClinical notesDischarge summariesElectronic medical record dataMedical record dataElectronic medical recordsMedication dataMedical recordsClinical dataClinical researchRecord dataHealthcare safetyDrug namesMedexF-measureClinical narrativesNatural language processing systemsInformation extraction system