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
Detecting abbreviations in discharge summaries using machine learning methods.
Wu Y, Rosenbloom S, Denny J, Miller R, Mani S, Giuse D, Xu H. Detecting abbreviations in discharge summaries using machine learning methods. AMIA Annual Symposium Proceedings 2011, 2011: 1541-9. PMID: 22195219, PMCID: PMC3243185.Peer-Reviewed Original ResearchConceptsNatural language processingMachine learning methodsHighest F-measureF-measureClinical natural language processingLexical resourcesClinical abbreviationsTraining setPre-defined featuresRandom forest classifierDomain expertsML algorithmsML classifiersLanguage processingVoting schemeLearning methodsDischarge summariesForest classifierTest setClassifierCorpus-based methodSetResourcesAlgorithmAbbreviations
2006
Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues
Xu H, Markatou M, Dimova R, Liu H, Friedman C. Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues. BMC Bioinformatics 2006, 7: 334. PMID: 16822321, PMCID: PMC1550263, DOI: 10.1186/1471-2105-7-334.Peer-Reviewed Original ResearchConceptsNatural language processingBiomedical domainInformation retrieval systemsML methodsWSD classifierSense disambiguationMachine learning methodsVector machine classifierError rateWord sense disambiguationRetrieval systemMachine learningML techniquesText miningBiomedical abbreviationsLanguage processingLearning methodsCross-validation methodWSD problemMachine classifierAccurate accessSense distributionClassifierBiomolecular entitiesWSD task