2024
A scoping review of fair machine learning techniques when using real-world data
Huang Y, Guo J, Chen W, Lin H, Tang H, Wang F, Xu H, Bian J. A scoping review of fair machine learning techniques when using real-world data. Journal Of Biomedical Informatics 2024, 151: 104622. PMID: 38452862, PMCID: PMC11146346, DOI: 10.1016/j.jbi.2024.104622.Peer-Reviewed Original ResearchConceptsReal-world dataHealth care applicationsHealth care domainMachine learningArtificial intelligenceCare applicationsMulti-modal dataIntegration of artificial intelligenceMachine learning techniquesPre-processing techniquesCare domainBias mitigation approachesPublic datasetsAI/ML modelsModel fairnessLearning techniquesOptimal fairnessHealth care dataAI toolsHealth careAlgorithmic biasML modelsAI/MLFairnessBias issues
2023
Prediction of Brain Metastases Development in Patients With Lung Cancer by Explainable Artificial Intelligence From Electronic Health Records
Li Z, Li R, Zhou Y, Rasmy L, Zhi D, Zhu P, Dono A, Jiang X, Xu H, Esquenazi Y, Zheng W. Prediction of Brain Metastases Development in Patients With Lung Cancer by Explainable Artificial Intelligence From Electronic Health Records. JCO Clinical Cancer Informatics 2023, 7: e2200141. PMID: 37018650, PMCID: PMC10281421, DOI: 10.1200/cci.22.00141.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceBrain NeoplasmsEarly Detection of CancerElectronic Health RecordsHumansLung NeoplasmsConceptsBrain metastasesExplainable artificial intelligenceFeature attribution methodsLung cancerEHR dataArtificial intelligenceCerner Health Facts databaseBM developmentExplainable artificial intelligence approachBrain metastasis developmentHealth Facts databaseElectronic health record dataRecurrent neural network modelArtificial intelligence approachHealth record dataModel decision processStructured EHR dataNeural network modelDecision processAttribution methodsHigh-quality cohortElectronic health recordsPrompt treatmentMetastasis developmentIntelligence approach
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 ResearchMeSH KeywordsArtificial IntelligenceCOVID-19Eligibility DeterminationHumansNatural Language ProcessingPatient SelectionConceptsNegation 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
2021
The application of artificial intelligence and data integration in COVID-19 studies: a scoping review
Guo Y, Zhang Y, Lyu T, Prosperi M, Wang F, Xu H, Bian J. The application of artificial intelligence and data integration in COVID-19 studies: a scoping review. Journal Of The American Medical Informatics Association 2021, 28: 2050-2067. PMID: 34151987, PMCID: PMC8344463, DOI: 10.1093/jamia/ocab098.Peer-Reviewed Original ResearchConceptsAI applicationsArtificial intelligenceData integrationHeterogeneous dataSocial media data analysisMost AI applicationsHeterogeneous data sourcesMedia data analysisProteomics data analysisAI algorithmsAI frameworkElectronic health recordsHeterogenous dataBiased algorithmsHealth recordsCOVID-19 researchData analysisSingle-source approachResearch topicData sourcesResearch areaIntelligenceSurveillance systemDifferent sourcesAlgorithm
2014
Automated Assessment of Medical Students' Clinical Exposures according to AAMC Geriatric Competencies.
Chen Y, Wrenn J, Xu H, Spickard A, Habermann R, Powers J, Denny J. Automated Assessment of Medical Students' Clinical Exposures according to AAMC Geriatric Competencies. AMIA Annual Symposium Proceedings 2014, 2014: 375-84. PMID: 25954341, PMCID: PMC4419906.Peer-Reviewed Original ResearchConceptsSelf-care capacityPalliative careClinical notesClinical exposureHealth care professionalsGait disordersMedication managementHospital careCare professionalsStudents' clinical exposureGeriatric competenciesBehavioral disordersAmerican AssociationCareDisordersCompetency domainsExposurePreliminary studyMedical studentsEvaluating Word Representation Features in Biomedical Named Entity Recognition Tasks
Tang B, Cao H, Wang X, Chen Q, Xu H. Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks. BioMed Research International 2014, 2014: 240403. PMID: 24729964, PMCID: PMC3963372, DOI: 10.1155/2014/240403.Peer-Reviewed Original ResearchConceptsBiomedical Named Entity RecognitionWord representationsNamed Entity Recognition (NER) taskMachine learning-based approachWord representation featuresNatural language processingLearning-based approachEntity recognition taskNamed Entity RecognitionCluster-based representationJNLPBA corpusEntity recognitionBiomedical domainF-measureLanguage processingRepresentation featuresWord embeddingsRecognition taskWR algorithmDistributional representationsTaskBetter performanceAlgorithmRepresentationDifferent typesDetermining molecular predictors of adverse drug reactions with causality analysis based on structure learning
Liu M, Cai R, Hu Y, Matheny M, Sun J, Hu J, Xu H. Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning. Journal Of The American Medical Informatics Association 2014, 21: 245-251. PMID: 24334612, PMCID: PMC3932464, DOI: 10.1136/amiajnl-2013-002051.Peer-Reviewed Original ResearchAssisted annotation of medical free text using RapTAT
Gobbel G, Garvin J, Reeves R, Cronin R, Heavirland J, Williams J, Weaver A, Jayaramaraja S, Giuse D, Speroff T, Brown S, Xu H, Matheny M. Assisted annotation of medical free text using RapTAT. Journal Of The American Medical Informatics Association 2014, 21: 833-841. PMID: 24431336, PMCID: PMC4147611, DOI: 10.1136/amiajnl-2013-002255.Peer-Reviewed Original Research
2013
A comprehensive study of named entity recognition in Chinese clinical text
Lei J, Tang B, Lu X, Gao K, Jiang M, Xu H. A comprehensive study of named entity recognition in Chinese clinical text. Journal Of The American Medical Informatics Association 2013, 21: 808-814. PMID: 24347408, PMCID: PMC4147609, DOI: 10.1136/amiajnl-2013-002381.Peer-Reviewed Original ResearchInterdisciplinary dialogue for education, collaboration, and innovation: Intelligent Biology and Medicine in and beyond 2013
Zhang B, Huang Y, McDermott J, Posey R, Xu H, Zhao Z. Interdisciplinary dialogue for education, collaboration, and innovation: Intelligent Biology and Medicine in and beyond 2013. BMC Genomics 2013, 14: s1. PMID: 24564388, PMCID: PMC4042234, DOI: 10.1186/1471-2164-14-s8-s1.Peer-Reviewed Original ResearchApplying 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 ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceElectronic Health RecordsGenetic Association StudiesHumansPhenotypeSupport Vector MachineConceptsActive 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 dataApplying active learning to supervised word sense disambiguation in MEDLINE
Chen Y, Cao H, Mei Q, Zheng K, Xu H. Applying active learning to supervised word sense disambiguation in MEDLINE. Journal Of The American Medical Informatics Association 2013, 20: 1001-1006. PMID: 23364851, PMCID: PMC3756255, DOI: 10.1136/amiajnl-2012-001244.Peer-Reviewed Original ResearchA hybrid system for temporal information extraction from clinical text
Tang B, Wu Y, Jiang M, Chen Y, Denny J, Xu H. A hybrid system for temporal information extraction from clinical text. Journal Of The American Medical Informatics Association 2013, 20: 828-835. PMID: 23571849, PMCID: PMC3756274, DOI: 10.1136/amiajnl-2013-001635.Peer-Reviewed Original ResearchMachine learning for predicting the response of breast cancer to neoadjuvant chemotherapy
Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy A, Abramson V, Bhave S, Levy M, Xu H, Yankeelov T. Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy. Journal Of The American Medical Informatics Association 2013, 20: 688-695. PMID: 23616206, PMCID: PMC3721158, DOI: 10.1136/amiajnl-2012-001332.Peer-Reviewed Original ResearchConceptsNeoadjuvant chemotherapyFeature selectionCycles of NACPredictive model buildingTime most patientsBreast cancer patientsImportant clinical problemCourse of therapyMachine learningDynamic contrast-enhanced MRIContrast-enhanced MRIQuantitative dynamic contrast-enhanced MRIMost patientsTreatment regimenCancer patientsClinical variablesTherapeutic responseBreast cancerPredictive modeling approachClinical problemData show promiseLogistic regressionPatientsMachineDiffusion-weighted MRI dataRecognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
Tang B, Cao H, Wu Y, Jiang M, Xu H. Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features. BMC Medical Informatics And Decision Making 2013, 13: s1. PMID: 23566040, PMCID: PMC3618243, DOI: 10.1186/1472-6947-13-s1-s1.Peer-Reviewed Original ResearchConceptsStructural support vector machineWord representation featuresClinical NER tasksConditional Random FieldsSupport vector machinePerformance of MLClinical NER systemMachine learningRepresentation featuresNER systemNER taskVector machineEntity recognitionNatural language processing researchSequential labeling algorithmClinical entity recognitionLarge margin theoryClinical text processingLanguage processing researchPerformance of CRFsHighest F-measureClinical NLP researchI2b2 NLP challengeSame feature setsBetter performance
2012
Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks
Han B, Chen X, Talebizadeh Z, Xu H. Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks. BMC Systems Biology 2012, 6: s14. PMID: 23281790, PMCID: PMC3524021, DOI: 10.1186/1752-0509-6-s3-s14.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAlzheimer DiseaseArtificial IntelligenceAutistic DisorderBayes TheoremComputational BiologyComputer SimulationDatabases, GeneticEpistasis, GeneticGenome-Wide Association StudyHumansMacular DegenerationMarkov ChainsModels, GeneticMonte Carlo MethodPolymorphism, Single NucleotideConceptsEpistatic interaction detectionBayesian network structure learning methodTwo-layer Bayesian networkBayesian network-based methodBayesian networkInteraction detectionMarkov chain Monte Carlo methodsStructure learning methodReal disease dataNetwork-based methodReal GWAS datasetMonte Carlo methodHigh-order epistatic interactionsMachine learningSearch spaceLearning methodsDisease datasetCarlo methodTarget nodeModel complexityStatistical methodsReal dataNew scoring functionComplex human diseasesDatasetExtracting semantic lexicons from discharge summaries using machine learning and the C-Value method.
Jiang M, Denny J, Tang B, Cao H, Xu H. Extracting semantic lexicons from discharge summaries using machine learning and the C-Value method. AMIA Annual Symposium Proceedings 2012, 2012: 409-16. PMID: 23304311, PMCID: PMC3540581.Peer-Reviewed Original ResearchLarge-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs
Liu M, Wu Y, Chen Y, Sun J, Zhao Z, Chen X, Matheny M, Xu H. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs. Journal Of The American Medical Informatics Association 2012, 19: e28-e35. PMID: 22718037, PMCID: PMC3392844, DOI: 10.1136/amiajnl-2011-000699.Peer-Reviewed Original ResearchConceptsAdverse drug reactionsPost-marketing phaseDrug reactionsSevere adverse drug reactionsImportant adverse drug reactionsWithdrawal of rofecoxibPotential adverse drug reactionsPost-marketing surveillanceADR predictionPatient morbidityClinical trialsMajor causeLarge-scale studiesDrugsMolecular pathwaysDrug developmentPhenotypic featuresSignificant improvementPhenotypic characteristicsEarly stagesRecognition 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 systemsExtracting epidemiologic exposure and outcome terms from literature using machine learning approaches.
Lu Y, Xu H, Peterson N, Dai Q, Jiang M, Denny J, Liu M. Extracting epidemiologic exposure and outcome terms from literature using machine learning approaches. International Journal Of Data Mining And Bioinformatics 2012, 6: 447-59. PMID: 23155773, DOI: 10.1504/ijdmb.2012.049284.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceEpidemiologic FactorsHumansInformation Storage and RetrievalKnowledge BasesNatural Language Processing