2024
Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature
Xiao Y, Hou Y, Zhou H, Diallo G, Fiszman M, Wolfson J, Zhou L, Kilicoglu H, Chen Y, Su C, Xu H, Mantyh W, Zhang R. Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature. Scientific Reports 2024, 14: 8693. PMID: 38622164, PMCID: PMC11018822, DOI: 10.1038/s41598-024-58604-8.Peer-Reviewed Original ResearchConceptsAlzheimer's diseaseManual therapy techniquesR-GCNKnowledge graphAD preventionNon-pharmacological interventionsBiomedical literatureGraph convolutional network modelKG embedding modelsTest setLink prediction modelIntegrated healthConvolutional network modelImprove cognitive functionHighest scoring candidatesDomain expertsEmbedding modelNon-pharmaceutical interventionsReal-world data analysisGround truthPrevent ADCognitive functionTherapy techniquesNetwork modelDiscovery patterns
2022
Combining human and machine intelligence for clinical trial eligibility querying
Fang Y, Idnay B, Sun Y, Liu H, Chen Z, Marder K, Xu H, Schnall R, Weng C. Combining human and machine intelligence for clinical trial eligibility querying. Journal Of The American Medical Informatics Association 2022, 29: 1161-1171. PMID: 35426943, PMCID: PMC9196697, DOI: 10.1093/jamia/ocac051.Peer-Reviewed Original ResearchConceptsNegation scope detectionCohort queriesScope detectionHealth Information Technology Usability Evaluation ScaleHuman-computer collaborationValue normalizationNatural language processingMachine intelligenceDomain expertsEligibility criteria textUsability evaluationLearnability scoreF1 scoreUser interventionLanguage processingHuman intelligenceUsability scoreQueriesError correctionEngagement featuresIntelligenceDisease trialsFrequent modificationsEnhanced modulesCOVID-19 clinical trials
2017
Expressing 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
2015
A study of active learning methods for named entity recognition in clinical text
Chen Y, Lasko T, Mei Q, Denny J, Xu H. A study of active learning methods for named entity recognition in clinical text. Journal Of Biomedical Informatics 2015, 58: 11-18. PMID: 26385377, PMCID: PMC4934373, DOI: 10.1016/j.jbi.2015.09.010.Peer-Reviewed Original ResearchConceptsClinical NER tasksMachine learningAnnotation costF-measureEntity recognitionNER taskActive learningLearning methodsI2b2/VA NLP challengeNatural language processing systemsPerformance of MLClinical natural language processing (NLP) systemsSequential labeling tasksSupervised machine learningAL methodsLanguage processing systemDiversity-based methodReal-time settingActive learning methodsNew AL methodsNER corpusDomain expertsUncertainty samplingAnnotation effortClinical text
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
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
2007
A study of abbreviations in clinical notes.
Xu H, Stetson P, Friedman C. A study of abbreviations in clinical notes. AMIA Annual Symposium Proceedings 2007, 2007: 821-5. PMID: 18693951, PMCID: PMC2655910.Peer-Reviewed Original ResearchConceptsUnified Medical Language SystemNatural language processing systemsLanguage processing systemNarrative clinical notesDetection methodClinical notesDifferent knowledge sourcesSense inventoryDomain expertsNLP systemsCorrect sensesDecision supportText corporaKnowledge sourcesError detectionProcessing systemBiomedical literatureStudy of abbreviationsLanguage systemPatient informationAmbiguity rateBetter detection methodsDatabaseAnnotationAbbreviations