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 patternsA 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 issuesMapping Clinical Documents to the Logical Observation Identifiers, Names and Codes (LOINC) Document Ontology using Electronic Health Record Systems Structured Metadata.
Khan H, Mosa A, Paka V, Rana M, Mandhadi V, Islam S, Xu H, McClay J, Sarker S, Rao P, Waitman L. Mapping Clinical Documents to the Logical Observation Identifiers, Names and Codes (LOINC) Document Ontology using Electronic Health Record Systems Structured Metadata. AMIA Annual Symposium Proceedings 2024, 2023: 1017-1026. PMID: 38222329, PMCID: PMC10785913.Peer-Reviewed Original ResearchConceptsDocument ontologyElectronic health recordsBag-of-words approachNatural language processing techniquesFree-text documentsLanguage processing techniquesClinical documentationLogical Observation IdentifiersText documentsStructured metadataWords approachComputational scalabilityMetadataHealth recordsEHR documentationElectronic health record fieldsProcessing techniquesOntologyDocumentsAutomated pipelineNLPScalabilityClinical careFrameworkLOINCStandardizing Multi-site Clinical Note Titles to LOINC Document Ontology: A Transformer-based Approach.
Zuo X, Zhou Y, Duke J, Hripcsak G, Shah N, Banda J, Reeves R, Miller T, Waitman L, Natarajan K, Xu H. Standardizing Multi-site Clinical Note Titles to LOINC Document Ontology: A Transformer-based Approach. AMIA Annual Symposium Proceedings 2024, 2023: 834-843. PMID: 38222429, PMCID: PMC10785935.Peer-Reviewed Original Research
2023
The All of Us Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research
Mayo K, Basford M, Carroll R, Dillon M, Fullen H, Leung J, Master H, Rura S, Sulieman L, Kennedy N, Banks E, Bernick D, Gauchan A, Lichtenstein L, Mapes B, Marginean K, Nyemba S, Ramirez A, Rotundo C, Wolfe K, Xia W, Azuine R, Cronin R, Denny J, Kho A, Lunt C, Malin B, Natarajan K, Wilkins C, Xu H, Hripcsak G, Roden D, Philippakis A, Glazer D, Harris P. The All of Us Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research. Annual Review Of Biomedical Data Science 2023, 6: 443-464. PMID: 37561600, PMCID: PMC11157478, DOI: 10.1146/annurev-biodatasci-122120-104825.Peer-Reviewed Original ResearchSystematic design and data-driven evaluation of social determinants of health ontology (SDoHO).
Dang Y, Li F, Hu X, Keloth V, Zhang M, Fu S, Amith M, Fan J, Du J, Yu E, Liu H, Jiang X, Xu H, Tao C. Systematic design and data-driven evaluation of social determinants of health ontology (SDoHO). Journal Of The American Medical Informatics Association 2023, 30: 1465-1473. PMID: 37301740, PMCID: PMC10436148, DOI: 10.1093/jamia/ocad096.Peer-Reviewed Original ResearchAutomated Identification of Missing IS-A Relations in the Human Phenotype Ontology.
Mohtashamian M, Hu R, Abeysinghe R, Hao X, Xu H, Cui L. Automated Identification of Missing IS-A Relations in the Human Phenotype Ontology. AMIA Annual Symposium Proceedings 2023, 2022: 785-794. PMID: 37128366, PMCID: PMC10148310.Peer-Reviewed Original ResearchRepresenting and utilizing clinical textual data for real world studies: An OHDSI approach
Keloth V, Banda J, Gurley M, Heider P, Kennedy G, Liu H, Liu F, Miller T, Natarajan K, V Patterson O, Peng Y, Raja K, Reeves R, Rouhizadeh M, Shi J, Wang X, Wang Y, Wei W, Williams A, Zhang R, Belenkaya R, Reich C, Blacketer C, Ryan P, Hripcsak G, Elhadad N, Xu H. Representing and utilizing clinical textual data for real world studies: An OHDSI approach. Journal Of Biomedical Informatics 2023, 142: 104343. PMID: 36935011, PMCID: PMC10428170, DOI: 10.1016/j.jbi.2023.104343.Peer-Reviewed Original ResearchConceptsNatural language processingCommon data modelTextual dataNLP solutionObservational Health Data SciencesOMOP Common Data ModelSpecific use casesObservational Medical Outcomes Partnership Common Data ModelHealth Data SciencesRepresentation of informationUse casesElectronic health recordsReal-world evidence generationData scienceClinical textData modelClinical notesLanguage processingHealth recordsLoad dataClinical documentationCurrent applicationsInformationWorkflowEvidence generation
2022
Assess the documentation of cognitive tests and biomarkers in electronic health records via natural language processing for Alzheimer’s disease and related dementias
Chen Z, Zhang H, Yang X, Wu S, He X, Xu J, Guo J, Prosperi M, Wang F, Xu H, Chen Y, Hu H, DeKosky S, Farrer M, Guo Y, Wu Y, Bian J. Assess the documentation of cognitive tests and biomarkers in electronic health records via natural language processing for Alzheimer’s disease and related dementias. International Journal Of Medical Informatics 2022, 170: 104973. PMID: 36577203, DOI: 10.1016/j.ijmedinf.2022.104973.Peer-Reviewed Original ResearchConceptsElectronic health recordsPatients' electronic health recordsCognitive testsCognitive test scoresFlorida health systemSeverity categoriesHealth recordsAD-related dementiaAD/ADRD researchAD/ADRDPatient levelAlzheimer's diseaseClinical narrativesHealth systemBiomarkersDifferent severityDiseaseSeverityPatientsADRD researchStandardized approachDementiaTest scoresPopulation characteristicsScoresDLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models
Luo C, Islam M, Sheils N, Buresh J, Reps J, Schuemie M, Ryan P, Edmondson M, Duan R, Tong J, Marks-Anglin A, Bian J, Chen Z, Duarte-Salles T, Fernández-Bertolín S, Falconer T, Kim C, Park R, Pfohl S, Shah N, Williams A, Xu H, Zhou Y, Lautenbach E, Doshi J, Werner R, Asch D, Chen Y. DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models. Nature Communications 2022, 13: 1678. PMID: 35354802, PMCID: PMC8967932, DOI: 10.1038/s41467-022-29160-4.Peer-Reviewed Original Research
2021
OncoSplicing: an updated database for clinically relevant alternative splicing in 33 human cancers
Zhang Y, Yao X, Zhou H, Wu X, Tian J, Zeng J, Yan L, Duan C, Liu H, Li H, Chen K, Hu Z, Ye Z, Xu H. OncoSplicing: an updated database for clinically relevant alternative splicing in 33 human cancers. Nucleic Acids Research 2021, 50: d1340-d1347. PMID: 34554251, PMCID: PMC8728274, DOI: 10.1093/nar/gkab851.Peer-Reviewed Original ResearchConceptsAlternative splicingCancer-specific splicing eventsDifferential alternative splicingHuman cancersTCGA tumor samplesSplicing differencesSplicing eventsProtein complexityAdjacent normal samplesSplicingGene expressionSplicing dataNormal samplesAbnormal splicingIntegrative viewMRNA levelsDifferential analysisTumor samplesTranscriptsNonselective beta‐blockers are associated with a lower risk of hepatocellular carcinoma among cirrhotic patients in the United States
Wijarnpreecha K, Li F, Xiang Y, Xu X, Zhu C, Maroufy V, Wang Q, Tao W, Dang Y, Pham H, Zhou Y, Li J, Zhang X, Xu H, Taner C, Yang L, Tao C. Nonselective beta‐blockers are associated with a lower risk of hepatocellular carcinoma among cirrhotic patients in the United States. Alimentary Pharmacology & Therapeutics 2021, 54: 481-492. PMID: 34224163, DOI: 10.1111/apt.16490.Peer-Reviewed Original ResearchConceptsRisk of HCCBeta-blocker groupHepatocellular carcinomaCirrhotic patientsCerner Health Facts databaseCox proportional hazards regressionCumulative HCC incidenceRetrospective cohort studyHealth Facts databasePopulation-based studyProportional hazards regressionKaplan-Meier estimatesEligible patientsCohort studyHCC incidenceFinal cohortHazards regressionHCC riskLower riskPropensity scorePatientsCarvedilolRiskFacts databaseGroup
2020
Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies
Rasmy L, Tiryaki F, Zhou Y, Xiang Y, Tao C, Xu H, Zhi D. Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies. Journal Of The American Medical Informatics Association 2020, 27: 1593-1599. PMID: 32930711, PMCID: PMC7647355, DOI: 10.1093/jamia/ocaa180.Peer-Reviewed Original ResearchConceptsUnified Medical Language SystemRecurrent neural networkNeural networkPrediction performanceLogistic regressionPredictive modelingDeep learningData aggregationElectronic health record dataMachine learningRisk predictionBetter prediction performanceDengue hemorrhagic feverHealth record dataEHR dataCancer predictionLarge vocabularyDifferent tasksPredictive modelHeart failureDiabetes patientsPancreatic cancerClinical dataHemorrhagic feverICD-9Efficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus
Li Y, Luo Y, Wampfler J, Rubinstein S, Tiryaki F, Ashok K, Warner J, Xu H, Yang P. Efficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus. JCO Clinical Cancer Informatics 2020, 4: cci.19.00147. PMID: 32364754, PMCID: PMC7265793, DOI: 10.1200/cci.19.00147.Peer-Reviewed Original ResearchConceptsNatural language processing toolsElectronic health recordsLanguage processing toolsGold standard dataUnstructured electronic health recordsProcessing toolsAmount of dataClinical notesStandard dataMayo Clinic electronic health recordsClinic's electronic health recordEnvironment toolsAccurate annotationHealth recordsInformatics toolsEffective analysisData setsTextual sourcesCorpusToolInformationData extractionSetExtractingAnnotationAchievability to Extract Specific Date Information for Cancer Research.
Wang L, Wampfler J, Dispenzieri A, Xu H, Yang P, Liu H. Achievability to Extract Specific Date Information for Cancer Research. AMIA Annual Symposium Proceedings 2020, 2019: 893-902. PMID: 32308886, PMCID: PMC7153063.Peer-Reviewed Original ResearchRelation Extraction from Clinical Narratives Using Pre-trained Language Models.
Wei Q, Ji Z, Si Y, Du J, Wang J, Tiryaki F, Wu S, Tao C, Roberts K, Xu H. Relation Extraction from Clinical Narratives Using Pre-trained Language Models. AMIA Annual Symposium Proceedings 2020, 2019: 1236-1245. PMID: 32308921, PMCID: PMC7153059.Peer-Reviewed Original ResearchConceptsPre-trained language modelsNatural language processingLanguage modelRE tasksNLP tasksClinical narrativesRecent deep learning methodsDeep learning methodsClinical NLP tasksRelation extraction taskTraditional word embeddingsTraditional machineExtraction taskArt performanceRelation extractionBERT modelLanguage processingLearning methodsWord embeddingsShared TaskPrevious stateBiomedical literatureDifferent implementationsTaskOpen domainElectronic Health Records for Drug Repurposing: Current Status, Challenges, and Future Directions
Xu H, Li J, Jiang X, Chen Q. Electronic Health Records for Drug Repurposing: Current Status, Challenges, and Future Directions. Clinical Pharmacology & Therapeutics 2020, 107: 712-714. PMID: 32012237, PMCID: PMC10815929, DOI: 10.1002/cpt.1769.Peer-Reviewed Original Research