2017
Interweaving Domain Knowledge and Unsupervised Learning for Psychiatric Stressor Extraction from Clinical Notes
Zhang O, Zhang Y, Xu J, Roberts K, Zhang X, Xu H. Interweaving Domain Knowledge and Unsupervised Learning for Psychiatric Stressor Extraction from Clinical Notes. Lecture Notes In Computer Science 2017, 10351: 396-406. DOI: 10.1007/978-3-319-60045-1_41.Peer-Reviewed Original ResearchNatural language processing systemsWord representation featuresPsychiatric stressorsLanguage processing systemDeep learningDomain knowledgeElectronic health recordsUnsupervised learningInexact matchingClinical notesF-measureRepresentation featuresProcessing systemHealth recordsPsychiatric notesImportant problemMultiple sourcesExperimental resultsLearningAlgorithmChallengesMatchingNarrative textStressor dataRecallClinical Word Sense Disambiguation with Interactive Search and Classification.
Wang Y, Zheng K, Xu H, Mei Q. Clinical Word Sense Disambiguation with Interactive Search and Classification. AMIA Annual Symposium Proceedings 2017, 2016: 2062-2071. PMID: 28269966, PMCID: PMC5333264.Peer-Reviewed Original ResearchConceptsDomain knowledgeHuman expertsWSD modelClinical textCurrent active learning methodsWord sense disambiguation systemNatural language processing applicationsMachine learning processLanguage processing applicationsWord sense disambiguationActive learning methodsContextual wordsInteractive searchWord ambiguityLearning methodsSense disambiguationProcessing applicationsAmbiguous instancesSearch processDisambiguation systemEvaluation corpusLearning processExpertsQueriesClassifierExpressing Biomedical Ontologies in Natural Language for Expert Evaluation.
Amith M, Manion F, Harris M, Zhang Y, Xu H, Tao C. Expressing Biomedical Ontologies in Natural Language for Expert Evaluation. 2017, 245: 838-842. PMID: 29295217, PMCID: PMC6644701.Peer-Reviewed Original Research
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
2013
Applying active learning to high-throughput phenotyping algorithms for electronic health records data
Chen Y, Carroll R, Hinz E, Shah A, Eyler A, Denny J, Xu H. Applying active learning to high-throughput phenotyping algorithms for electronic health records data. Journal Of The American Medical Informatics Association 2013, 20: e253-e259. PMID: 23851443, PMCID: PMC3861916, DOI: 10.1136/amiajnl-2013-001945.Peer-Reviewed Original ResearchConceptsActive learningUnrefined featuresSupervised Machine Learning AlgorithmsRefined featuresPhenotyping algorithmElectronic health record dataMachine Learning AlgorithmsHealth record dataVenous thromboembolismRheumatoid arthritisFeature engineeringDomain expertsDomain knowledgePhenotyping tasksLearning algorithmFeature setsLearning approachColorectal cancerAL approachCurve scorePassive learning approachHigh-throughput phenotyping methodsAlgorithmSmall setRecord data