2016
Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature
Zhang Y, Wu H, Xu J, Wang J, Soysal E, Li L, Xu H. Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature. BMC Systems Biology 2016, 10: 67. PMID: 27585838, PMCID: PMC5009562, DOI: 10.1186/s12918-016-0311-2.Peer-Reviewed Original ResearchConceptsPaths graph kernelGraph kernelsSemantic classesSemantic informationBiomedical literatureShallow semantic representationsText mining techniquesBest F-scoreAutomatic DDI extractionProblem of sparsenessDependency structureSemantic graphDDI detectionKnowledge basesDDI corpusF-scoreDDI extractionSemantic representationNovel approachExperimental resultsKernelHigh precisionInformationSparsenessGraph
2012
Recognition 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