2019
Cost-sensitive Active Learning for Phenotyping of Electronic Health Records.
Ji Z, Wei Q, Franklin A, Cohen T, Xu H. Cost-sensitive Active Learning for Phenotyping of Electronic Health Records. AMIA Joint Summits On Translational Science Proceedings 2019, 2019: 829-838. PMID: 31259040, PMCID: PMC6568101.Peer-Reviewed Original ResearchAnnotation timeElectronic health recordsActive learningMachine learning-based methodsCost-sensitive active learningLarge annotated datasetLearning-based methodsHealth recordsUse casesAnnotated datasetUser 1AL algorithmUser 2Phenotyping algorithmAL approachSecondary useAlgorithmBetter performanceActual timeLearningExperimental resultsBreast cancer patientsDatasetModel performancePassive learning
2018
Adapting Word Embeddings from Multiple Domains to Symptom Recognition from Psychiatric Notes.
Zhang Y, Li H, Wang J, Cohen T, Roberts K, Xu H. Adapting Word Embeddings from Multiple Domains to Symptom Recognition from Psychiatric Notes. AMIA Joint Summits On Translational Science Proceedings 2018, 2017: 281-289. PMID: 29888086, PMCID: PMC5961810.Peer-Reviewed Original ResearchWord embeddingsClinical textTarget domainSource domainNatural language processing techniquesLanguage processing techniquesMultiple word embeddingsBaseline methodsBiomedical literatureFirst workProcessing techniquesEmbeddingPsychiatric notesMultiple domainsExperimental resultsDifferent weightsSuch informationImportant topicRecognitionDifferent approachesWikipediaInformationPersonalizationDomainText
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 dataRecall
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