2024
Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data
He X, Wei R, Huang Y, Chen Z, Lyu T, Bost S, Tong J, Li L, Zhou Y, Li Z, Guo J, Tang H, Wang F, DeKosky S, Xu H, Chen Y, Zhang R, Xu J, Guo Y, Wu Y, Bian J. Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data. Alzheimer's & Dementia Diagnosis Assessment & Disease Monitoring 2024, 16: e12613. PMID: 38966622, PMCID: PMC11220631, DOI: 10.1002/dad2.12613.Peer-Reviewed Original ResearchElectronic health record dataElectronic health recordsComputable phenotypeHealth record dataManual chart reviewHealth recordsAlzheimer's diseaseDiagnosis codesRecord dataChart reviewUTHealthAlzheimer's disease patientsUniversity of MinnesotaAD diagnosisAD identificationDisease patientsPatientsAlzheimerAD patientsDemographicsDiagnosisDiseaseCodeDataUniversity
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, PMCID: PMC11325083, 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 characteristicsScoresAssociations Between Vascular Diseases and Alzheimer’s Disease or Related Dementias in a Large Cohort of Men and Women with Colorectal Cancer
Du X, Song L, Schulz P, Xu H, Chan W. Associations Between Vascular Diseases and Alzheimer’s Disease or Related Dementias in a Large Cohort of Men and Women with Colorectal Cancer. Journal Of Alzheimer's Disease 2022, 90: 211-231. PMID: 36093703, PMCID: PMC9661325, DOI: 10.3233/jad-220548.Peer-Reviewed Original ResearchConceptsColorectal cancerVascular diseaseCardiovascular diseaseAlzheimer's diseaseRisk of ADSignificant dose-response relationshipRetrospective cohort studyCohort of patientsTypes of dementiaLong-term riskDose-response relationshipRisk of ADRDTumor factorsCohort studyCumulative incidenceOlder patientsLarge cohortPatientsRelated dementiaHypertensionCancerDiseaseDiabetesDementiaStroke
2021
Nonselective 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
A Natural Language Processing Tool to Extract Quantitative Smoking Status from Clinical Narratives
Yang X, Yang H, Lyu T, Yang S, Guo Y, Bian J, Xu H, Wu Y. A Natural Language Processing Tool to Extract Quantitative Smoking Status from Clinical Narratives. 2020 IEEE International Conference On Healthcare Informatics (ICHI) 2020, 00: 1-2. PMID: 33786419, PMCID: PMC8006894, DOI: 10.1109/ichi48887.2020.9374369.Peer-Reviewed Original Research
2019
Utilizing Natural Language Processing to Identify Diabetes Mellitus among Patients with Major Depressive Disorder During Psychiatric Hospitalization
Hamilton J, Zhang Y, Selek S, Roberts K, Lavagnino L, Xu H. Utilizing Natural Language Processing to Identify Diabetes Mellitus among Patients with Major Depressive Disorder During Psychiatric Hospitalization. Journal Of Affective Disorders 2019, 254: 142. DOI: 10.1016/j.jad.2018.10.315.Peer-Reviewed Original ResearchDiscovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing
Wu Y, Warner J, Wang L, Jiang M, Xu J, Chen Q, Nian H, Dai Q, Du X, Yang P, Denny J, Liu H, Xu H. Discovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing. JCO Clinical Cancer Informatics 2019, 3: cci.19.00001. PMID: 31141421, PMCID: PMC6693869, DOI: 10.1200/cci.19.00001.Peer-Reviewed Original ResearchConceptsVanderbilt University Medical CenterCancer survivalMayo ClinicDrug repurposingNoncancer drugsElectronic health record dataCancer registry dataEHR dataClinical trial evaluationOverall cancer survivalUniversity Medical CenterHealth record dataElectronic health recordsTreatment of cancerClinical trialsDrug classesRegistry dataMedical CenterDrug effectsSignificant associationLongitudinal EHRNew indicationsPatientsCancerHealth records
2018
Analysis of treatment pathways for three chronic diseases using OMOP CDM
Zhang X, Wang L, Miao S, Xu H, Yin Y, Zhu Y, Dai Z, Shan T, Jing S, Wang J, Zhang X, Huang Z, Wang Z, Guo J, Liu Y. Analysis of treatment pathways for three chronic diseases using OMOP CDM. Journal Of Medical Systems 2018, 42: 260. PMID: 30421323, PMCID: PMC6244882, DOI: 10.1007/s10916-018-1076-5.Peer-Reviewed Original ResearchConceptsTreatment pathwaysChronic diseasesStudy of drugsClinical data repositoryClinical treatmentDifferent medical institutionsProportion of monotherapyFirst-line medicationMedical institutionsFirst Affiliated HospitalType 2 diabetesNanjing Medical UniversityDifferent treatment pathwaysMost patientsCommon medicationsAffiliated HospitalMedicationsNational guidelinesMedication informationLocal hospitalMedical UniversitySame diseaseDiseasePatientsNew drugsLeveraging existing corpora for de-identification of psychiatric notes using domain adaptation.
Lee H, Zhang Y, Roberts K, Xu H. Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation. AMIA Annual Symposium Proceedings 2018, 2017: 1070-1079. PMID: 29854175, PMCID: PMC5977650.Peer-Reviewed Original Research
2017
Identifying Metastases-related Information from Pathology Reports of Lung Cancer Patients.
Soysal E, Warner J, Denny J, Xu H. Identifying Metastases-related Information from Pathology Reports of Lung Cancer Patients. AMIA Joint Summits On Translational Science Proceedings 2017, 2017: 268-277. PMID: 28815141, PMCID: PMC5543353.Peer-Reviewed Original ResearchPathology reportsSpecimen siteImportant prognostic factorLung cancer patientsMetastatic patternPrognostic factorsClinical courseHistological typeCancer patientsMetastasis sitesMetastatic statusCancer recurrenceCancer metastasisMetastasisTumor metastasisPatientsStatus indicatorsReportStatusRecurrence
2015
Effects of Health Insurance on Tumor Stage, Treatment, and Survival in Large Cohorts of Patients with Breast and Colorectal Cancer
Zhang Y, Franzini L, Chan W, Xu H, Du X. Effects of Health Insurance on Tumor Stage, Treatment, and Survival in Large Cohorts of Patients with Breast and Colorectal Cancer. Journal Of Health Care For The Poor And Underserved 2015, 26: 1336-1358. PMID: 26548682, DOI: 10.1353/hpu.2015.0119.Peer-Reviewed Original ResearchConceptsRisk of mortalityColorectal cancerTumor stagePrivate health insuranceCancer patientsHealth insuranceCancer-directed surgeryColorectal cancer patientsTexas Cancer RegistryInsurance coverageAdditional private health insuranceBreast cancer patientsHealth insurance statusHealth insurance coverageOverall survivalCancer RegistryInsurance statusBreast cancerLarge cohortHigh riskMedicare beneficiariesPatientsCancerChemotherapySurgeryIdentifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2
Stubbs A, Kotfila C, Xu H, Uzuner Ö. Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2. Journal Of Biomedical Informatics 2015, 58: s67-s77. PMID: 26210362, PMCID: PMC4978189, DOI: 10.1016/j.jbi.2015.07.001.Peer-Reviewed Original ResearchMeSH KeywordsAgedBostonCohort StudiesComorbidityComputer SecurityConfidentialityCoronary Artery DiseaseData MiningDiabetes ComplicationsElectronic Health RecordsFemaleHumansIncidenceLongitudinal StudiesMaleMiddle AgedNarrationNatural Language ProcessingPattern Recognition, AutomatedRisk AssessmentVocabulary, ControlledConceptsCoronary artery diseaseRisk factorsLongitudinal medical recordsMedical recordsMedical risk factorsArtery diseaseDiabetic patientsSmoking statusHeart diseaseFamily historyI2b2/UTHealth natural language processingDiseaseI2b2/UTHealthProgressionUTHealthHypertensionHyperlipidemiaFactorsObesityDiabetesPatientsTrends and variations in breast and colorectal cancer incidence from 1995 to 2011: A comparative study between Texas Cancer Registry and National Cancer Institute’s Surveillance, Epidemiology and End Results data
LIU Z, ZHANG Y, FRANZIN L, CORMIER J, CHAN W, XU H, DU X. Trends and variations in breast and colorectal cancer incidence from 1995 to 2011: A comparative study between Texas Cancer Registry and National Cancer Institute’s Surveillance, Epidemiology and End Results data. International Journal Of Oncology 2015, 46: 1819-1826. PMID: 25672365, PMCID: PMC4356494, DOI: 10.3892/ijo.2015.2881.Peer-Reviewed Original ResearchConceptsColorectal cancer incidenceNational Cancer Institute's SurveillanceTexas Cancer RegistryBreast cancer incidenceCancer incidenceCancer RegistryAge-adjusted breast cancer incidenceColorectal cancer patientsEnd Results (SEER) dataSEER areasColorectal cancerCancer patientsIncidence rateRelative riskIncidenceBreastRegistrySurveillanceEpidemiologySEERResult dataTemporal trendsEnd resultPatientsParallel comparison
2013
Machine 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 data
2012
Optimizing Drug Outcomes Through Pharmacogenetics: A Case for Preemptive Genotyping
Schildcrout J, Denny J, Bowton E, Gregg W, Pulley J, Basford M, Cowan J, Xu H, Ramirez A, Crawford D, Ritchie M, Peterson J, Masys D, Wilke R, Roden D. Optimizing Drug Outcomes Through Pharmacogenetics: A Case for Preemptive Genotyping. Clinical Pharmacology & Therapeutics 2012, 92: 235-242. PMID: 22739144, PMCID: PMC3785311, DOI: 10.1038/clpt.2012.66.Peer-Reviewed Original ResearchConceptsVanderbilt University Medical CenterAdverse eventsPreemptive genotypingPotential adverse eventsUniversity Medical CenterHome patientsPharmacogenetic associationsMedical CenterVariant allelesMedicationsDrug outcomesPatient safetyDrug decision makingRelevant genetic variantsRoutine integrationTarget drugsGenetic variantsOutcomesFrequency of opportunitiesGenotypingSafetyPrescribingPatientsCohortPharmacogeneticsUnderstanding patient‐provider communication entered via a patient portal system
Sun S, Zhou X, Denny J, Rosenbloom T, Xu H. Understanding patient‐provider communication entered via a patient portal system. Proceedings Of The American Society For Information Science And Technology 2012, 49: 1-4. DOI: 10.1002/meet.14504901175.Peer-Reviewed Original ResearchUnderstanding patient‐provider communication entered via a patient portal system
Sun S, Zhou X, Denny J, Rosenbloom T, Xu H. Understanding patient‐provider communication entered via a patient portal system. Proceedings Of The American Society For Information Science And Technology 2012, 49: 1-4. DOI: 10.1002/meet.14504901387.Peer-Reviewed Original Research
2010
Extracting timing and status descriptors for colonoscopy testing from electronic medical records
Denny J, Peterson J, Choma N, Xu H, Miller R, Bastarache L, Peterson N. Extracting timing and status descriptors for colonoscopy testing from electronic medical records. Journal Of The American Medical Informatics Association 2010, 17: 383-388. PMID: 20595304, PMCID: PMC2995656, DOI: 10.1136/jamia.2010.004804.Peer-Reviewed Original ResearchConceptsElectronic medical recordsMedical recordsColorectal cancer screening ratesCRC screening statusCancer screening ratesManual reviewStatus indicatorsHealth services researchersColonoscopy testingEMR notesTypes of CRCScreening statusScreening ratesColonoscopy screeningBilling codesUseful adjunctGold standardElectronic recordsColonoscopyPatientsServices researchersFurther investigationRandom sampleTemporal expression
2004
Facilitating cancer research using natural language processing of pathology reports.
Xu H, Anderson K, Grann V, Friedman C. Facilitating cancer research using natural language processing of pathology reports. 2004, 107: 565-72. PMID: 15360876.Peer-Reviewed Original Research