2020
A study of entity-linking methods for normalizing Chinese diagnosis and procedure terms to ICD codes
Wang Q, Ji Z, Wang J, Wu S, Lin W, Li W, Ke L, Xiao G, Jiang Q, Xu H, Zhou Y. A study of entity-linking methods for normalizing Chinese diagnosis and procedure terms to ICD codes. Journal Of Biomedical Informatics 2020, 105: 103418. PMID: 32298846, DOI: 10.1016/j.jbi.2020.103418.Peer-Reviewed Original ResearchConceptsBM25 algorithmConcept rankingConcept generationConvolutional neural network approachNeural network approachRanking-based methodRanking methodSupport vector machineProcedure termsBetter performanceVector machineDifferent algorithmsMedical codingNetwork approachAlgorithmICD codesBERTExtended versionGood accuracyKnowledgebaseDisease termsClinical termsMatch criteriaCodeChinese diagnosis
2019
Temporal indexing of medical entity in Chinese clinical notes
Liu Z, Wang X, Chen Q, Tang B, Xu H. Temporal indexing of medical entity in Chinese clinical notes. BMC Medical Informatics And Decision Making 2019, 19: 17. PMID: 30700331, PMCID: PMC6354334, DOI: 10.1186/s12911-019-0735-x.Peer-Reviewed Original ResearchConceptsSupport vector machineConvolutional neural networkTemporal indexingNeural network modelIndexing taskRelation classificationMedical entitiesRecurrent convolutional neural network modelMachine learning-based systemsConvolutional neural network modelDeep neural network modelNetwork methodNetwork modelLearning-based systemTemporal relation classificationRecurrent neural network methodChinese clinical notesTemporal relationsClinical notesNeural network methodI2b2 NLP challengeContext informationTime indexingSemantic informationBaseline methods
2018
Extracting psychiatric stressors for suicide from social media using deep learning
Du J, Zhang Y, Luo J, Jia Y, Wei Q, Tao C, Xu H. Extracting psychiatric stressors for suicide from social media using deep learning. BMC Medical Informatics And Decision Making 2018, 18: 43. PMID: 30066665, PMCID: PMC6069295, DOI: 10.1186/s12911-018-0632-8.Peer-Reviewed Original ResearchConceptsConvolutional neural networkRecurrent neural networkDeep learningConditional Random FieldsSupport vector machineSuicide-related tweetsClinical textNeural networkPsychiatric stressorsExtra TreesBinary classifierTransfer learning strategiesEntity recognition taskSocial mediaExact matchTraditional machineAnnotation costLearning strategiesRecognition problemSharing flowInexact matchVector machineTwitter dataRecognition taskTwitter
2017
Entity recognition from clinical texts via recurrent neural network
Liu Z, Yang M, Wang X, Chen Q, Tang B, Wang Z, Xu H. Entity recognition from clinical texts via recurrent neural network. BMC Medical Informatics And Decision Making 2017, 17: 67. PMID: 28699566, PMCID: PMC5506598, DOI: 10.1186/s12911-017-0468-7.Peer-Reviewed Original ResearchConceptsRecurrent neural networkNatural language processingEntity recognitionClinical textTraditional machineNeural networkClinical natural language processingMedical concept extractionHand-crafted featuresClinical entity recognitionDeep learning methodsClinical event detectionConditional Random FieldsSupport vector machineI2b2 NLP challengePerformance of LSTMTypes of entitiesClinical domainsContext informationFeature engineeringConcept extractionDe-identificationEvent detectionKnowledge basesLSTM layers
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 taskCD-REST: a system for extracting chemical-induced disease relation in literature
Xu J, Wu Y, Zhang Y, Wang J, Lee H, Xu H. CD-REST: a system for extracting chemical-induced disease relation in literature. Database 2016, 2016: baw036. PMID: 27016700, PMCID: PMC4808251, DOI: 10.1093/database/baw036.Peer-Reviewed Original ResearchConceptsChemical-induced disease relationsWeb servicesBiomedical literatureEntity recognitionMachine learning-based approachLearning-based approachHTTP POST requestRelation extraction systemVector space modelConditional Random FieldsSupport vector machineRelation extraction moduleVast biomedical literatureDisease relation extractionChemical-induced disease relation extractionExtraction moduleDisease relationsAutomatic extractionEnd systemPOST requestRelation extractionNormalization moduleVector machineBioCreative VDemonstration system
2015
Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations
Chen Y, Sun J, Huang L, Xu H, Zhao Z. Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations. BioMed Research International 2015, 2015: 491502. PMID: 26539502, PMCID: PMC4619847, DOI: 10.1155/2015/491502.Peer-Reviewed Original ResearchConceptsMachine learningF-measureAvailable big dataSupport vector machineBig dataVector machineClassification experimentsAccurate classificationCancer classificationGene function informationMachineSomatic mutation informationClassificationMutation informationFunction informationLearningGene symbolsInformationGene featuresGreat opportunityPerformanceSomatic mutation dataMutation dataAccuracyPredictionA comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature
Tang B, Feng Y, Wang X, Wu Y, Zhang Y, Jiang M, Wang J, Xu H. A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature. Journal Of Cheminformatics 2015, 7: s8. PMID: 25810779, PMCID: PMC4331698, DOI: 10.1186/1758-2946-7-s1-s8.Peer-Reviewed Original ResearchMachine learning-based systemsConditional Random FieldsLearning-based systemEntity recognition systemSupport vector machineEntity recognitionRecognition systemF-measureChallenge organizersDrug Named Entity RecognitionVector machineStructured support vector machineMicro F-measureInformation extraction tasksWord representation featuresNamed Entity RecognitionTest setRandom fieldsPrimary evaluation measureBrown clusteringDocument indexingIndividual subtasksExtraction taskRandom IndexingBiomedical domain
2013
Word Sense Disambiguation of clinical abbreviations with hyperdimensional computing.
Moon S, Berster B, Xu H, Cohen T. Word Sense Disambiguation of clinical abbreviations with hyperdimensional computing. AMIA Annual Symposium Proceedings 2013, 2013: 1007-16. PMID: 24551390, PMCID: PMC3900125.Peer-Reviewed Original ResearchConceptsWord sense disambiguationAverage accuracySense disambiguationWord sense disambiguation algorithmSupport vector machineHyperdimensional ComputingNaïve BayesCommon machineClinical documentsVector machineDisambiguation algorithmClinical abbreviationsMedical informationAccurate extractionAlgorithmDisambiguationMachineSuch approachesClinical notesPresent new approachVector transformationNew approachAmbiguous termsComputingAccuracyRecognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
Tang B, Cao H, Wu Y, Jiang M, Xu H. Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features. BMC Medical Informatics And Decision Making 2013, 13: s1. PMID: 23566040, PMCID: PMC3618243, DOI: 10.1186/1472-6947-13-s1-s1.Peer-Reviewed Original ResearchConceptsStructural support vector machineWord representation featuresClinical NER tasksConditional Random FieldsSupport vector machinePerformance of MLClinical NER systemMachine learningRepresentation featuresNER systemNER taskVector machineEntity recognitionNatural language processing researchSequential labeling algorithmClinical entity recognitionLarge margin theoryClinical text processingLanguage processing researchPerformance of CRFsHighest F-measureClinical NLP researchI2b2 NLP challengeSame feature setsBetter performance
2012
Clinical entity recognition using structural support vector machines with rich features
Tang B, Cao H, Wu Y, Jiang M, Xu H. Clinical entity recognition using structural support vector machines with rich features. 2012, 13-20. DOI: 10.1145/2390068.2390073.Peer-Reviewed Original ResearchStructural support vector machineClinical entity recognitionSupport vector machineConditional Random FieldsNatural language processingEntity recognitionVector machineRich featuresNLP challengeSequential labeling algorithmLarge margin theoryUnsupervised word representationsClinical text processingConcept extraction taskLess training timeHighest F-measureTest setI2b2 NLP challengeExtraction taskTypical machineNER taskClinical textTraining timeF-measureLanguage processingRecognition of medication information from discharge summaries using ensembles of classifiers
Doan S, Collier N, Xu H, Duy P, Phuong T. Recognition of medication information from discharge summaries using ensembles of classifiers. BMC Medical Informatics And Decision Making 2012, 12: 36. PMID: 22564405, PMCID: PMC3502425, DOI: 10.1186/1472-6947-12-36.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceDecision Support TechniquesFemaleHumansInformation Storage and RetrievalInstitutional Management TeamsMaleMedication SystemsNatural Language ProcessingPatient DischargePattern Recognition, AutomatedPharmaceutical PreparationsReproducibility of ResultsSemanticsSoftware DesignSupport Vector MachineConceptsConditional Random FieldsNatural language processingClinical natural language processingSupport vector machineBest F-scoreEnsemble classifierF-scoreClinical textIndividual classifiersVoting methodMajority votingLocal support vector machineSupervised machine learning methodsClinical entity recognitionClinical NLP systemsDifferent voting strategiesEntity recognition systemRule-based systemEnsemble of classifiersMachine learning methodsRule-based methodI2b2 NLP challengeEntity recognitionRecognition systemNLP systems
2011
A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries
Jiang M, Chen Y, Liu M, Rosenbloom S, Mani S, Denny J, Xu H. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. Journal Of The American Medical Informatics Association 2011, 18: 601-606. PMID: 21508414, PMCID: PMC3168315, DOI: 10.1136/amiajnl-2011-000163.Peer-Reviewed Original ResearchConceptsEntity extraction systemCenter of InformaticsConcept extractionIntegrating BiologyEntity recognition moduleEntity recognition systemConditional Random FieldsOverall F-scoreSupport vector machineRule-based moduleAssertion classificationClassification taskRecognition moduleRecognition systemML algorithmsSemantic informationTraining dataClinical textNatural languageF-measureChallenge organizersF-scoreVector machineEvaluation scriptsTraining corpus
2010
Recognizing Medication related Entities in Hospital Discharge Summaries using Support Vector Machine.
Doan S, Xu H. Recognizing Medication related Entities in Hospital Discharge Summaries using Support Vector Machine. Proceedings - International Conference On Computational Linguistics 2010, 2010: 259-266. PMID: 26848286, PMCID: PMC4736747.Peer-Reviewed Original ResearchSupport vector machineHospital discharge summariesConditional Random FieldsDischarge summariesMedication namesRelated entitiesClinical textVector machineType of medicationNamed Entity Recognition (NER) taskEntity recognition taskRule-based systemBest F-scoreI2b2 NLP challengeTypes of featuresF-scoreI2b2 challengeNLP challengeNER systemSemantic featuresRecognition taskMachineData setsRandom fieldsBetter performance