2024
Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data
Lu Y, Tong J, Chubak J, Lumley T, Hubbard R, Xu H, Chen Y. Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data. Journal Of Biomedical Informatics 2024, 157: 104690. PMID: 39004110, DOI: 10.1016/j.jbi.2024.104690.Peer-Reviewed Original ResearchElectronic health recordsElectronic health record dataKaiser Permanente WashingtonEHR-derived phenotypesAssociation studiesHealth recordsColon cancer recurrencePhenotyping errorsComputable phenotypeRisk factorsCancer recurrenceMultiple phenotypesReduce biasImprove estimation accuracySimulation studyBias reductionKaiserReduction of biasBiasEstimation accuracyAssociationStudyOutcomesRiskEstimation efficiency
2023
Social and Behavior Factors of Alzheimer's Disease and Related Dementias: A National Study in the U.S.
Ciciora D, Vásquez E, Valachovic E, Hou L, Zheng Y, Xu H, Jiang X, Huang K, Gabriel K, Deng H, Gallant M, Zhang K. Social and Behavior Factors of Alzheimer's Disease and Related Dementias: A National Study in the U.S. American Journal Of Preventive Medicine 2023, 66: 573-581. PMID: 37995949, DOI: 10.1016/j.amepre.2023.11.017.Peer-Reviewed Original ResearchLevels of disadvantageRisk factorsPrevention strategiesCounty levelCounty-level measuresPrimary prevention strategiesPopulation-based studyHigh school diplomaADRDLifestyle factorsRelated dementiaDisadvantaged countiesSchool diplomaBehavioral factorsNational studyAlzheimer's diseaseDemographic variablesInsufficient sleepFactor of Alzheimer's diseaseCountyRiskSleepEffects of sleepEducationEnvironmental factors
2022
Factors Associated With COVID-19 Death in the United States: Cohort Study
Chen U, Xu H, Krause T, Greenberg R, Dong X, Jiang X. Factors Associated With COVID-19 Death in the United States: Cohort Study. JMIR Public Health And Surveillance 2022, 8: e29343. PMID: 35377319, PMCID: PMC9132142, DOI: 10.2196/29343.Peer-Reviewed Original ResearchConceptsCOVID-19-related deathsCohort studyPatient characteristicsElectronic health record data setsLarge national cohort studyNational cohort studyChronic respiratory diseasesProportional hazards modelCOVID-19COVID-19 deathsImmunosuppressive conditionsKidney functionMale sexLung cancerCOVID-19 casesRecent diagnosisRisk factorsRespiratory diseaseCardiac diseaseOrgan transplantsHigh incidenceHazards modelNeurological diseasesOlder ageDisease
2021
Leveraging a health information exchange for analyses of COVID-19 outcomes including an example application using smoking history and mortality
Tortolero G, Brown M, Sharma S, de Oliveira Otto M, Yamal J, Aguilar D, Gunther M, Mofleh D, Harris R, John J, de Vries P, Ramphul R, Serbo D, Kiger J, Banerjee D, Bonvino N, Merchant A, Clifford W, Mikhail J, Xu H, Murphy R, Wei Q, Vahidy F, Morrison A, Boerwinkle E. Leveraging a health information exchange for analyses of COVID-19 outcomes including an example application using smoking history and mortality. PLOS ONE 2021, 16: e0247235. PMID: 34081724, PMCID: PMC8174716, DOI: 10.1371/journal.pone.0247235.Peer-Reviewed Original ResearchConceptsBody mass indexCOVID-19 patientsRisk factorsTobacco useCOVID-19 fatalitiesHealth information exchangeRace/ethnicityCOVID-19Laboratory risk factorsNumber of comorbiditiesCOVID-19 cohortMultivariable logistic regressionImportant risk factorPotential risk factorsCOVID-19 outcomesFormer tobacco usersTobacco use historyLarge health information exchangeMass indexElectronic health record systemsUnfavorable outcomeClinical dataTobacco usersOutcome analysisElectronic health information
2020
Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study
Xiang Y, Ji H, Zhou Y, Li F, Du J, Rasmy L, Wu S, Zheng W, Xu H, Zhi D, Zhang Y, Tao C. Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study. Journal Of Medical Internet Research 2020, 22: e16981. PMID: 32735224, PMCID: PMC7428917, DOI: 10.2196/16981.Peer-Reviewed Original ResearchConceptsAttentive Neural NetworkAsthma exacerbationsRisk factorsNeural networkAdvanced deep learning modelsClinical variablesDeep learning modelsCerner Health Facts databaseLarge electronic health recordNeural network modelRetrospective cohort studyHealth Facts databasePotential risk factorsRisk factor analysisPersonalized risk factorsElectronic health recordsBaseline methodsLearning modelPersonalized risk scoreProgressive asthmaAsthma symptomsEsophageal refluxAdult patientsCohort studyTime-Sensitive
2019
Exploring temporal suicidal behavior patterns on social media: Insight from Twitter analytics
Luo J, Du J, Tao C, Xu H, Zhang Y. Exploring temporal suicidal behavior patterns on social media: Insight from Twitter analytics. Health Informatics Journal 2019, 26: 738-752. PMID: 30866708, DOI: 10.1177/1460458219832043.Peer-Reviewed Original ResearchConceptsRisk factorsDifferent risk factorsPotential suicidal ideationPublic health servicesKey risk factorsSuicide-related tweetsPrevention strategiesHealth servicesSuicidal ideationSuicide behaviorSuicide detectionSuicide ratesDifferent daysWider populationTemporal patternsBehavior patternsPopulationFactorsWeeks
2018
Psychiatric stressor recognition from clinical notes to reveal association with suicide
Zhang Y, Zhang O, Li R, Flores A, Selek S, Zhang X, Xu H. Psychiatric stressor recognition from clinical notes to reveal association with suicide. Health Informatics Journal 2018, 25: 1846-1862. PMID: 30328378, DOI: 10.1177/1460458218796598.Peer-Reviewed Original ResearchConceptsElectronic health recordsSuicidal behaviorHealth recordsSuicide ideation/attemptsTremendous economic burdenPsychiatric stressorsSuicide risk factorsRisk factorsEconomic burdenPsychiatric stressClinical notesLarge-scale studiesPsychiatric notesSuicideAssociationSignificant stressorsStressorsPrior studiesPercentPrevious studiesStudyExploring Temporal Patterns of Suicidal Behavior on Twitter
Luo J, Du J, Tao C, Xu H, Zhang Y. Exploring Temporal Patterns of Suicidal Behavior on Twitter. 2018, 55-56. DOI: 10.1109/ichi-w.2018.00017.Peer-Reviewed Original Research
2017
A Pilot Study of Mining Association Between Psychiatric Stressors and Symptoms in Tweets
Du J, Zhang Y, Tao C, Xu H. A Pilot Study of Mining Association Between Psychiatric Stressors and Symptoms in Tweets. 2017, 1254-1257. DOI: 10.1109/bibm.2017.8217838.Peer-Reviewed Original Research
2015
Identifying 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/UTHealthProgressionUTHealthHypertensionHyperlipidemiaFactorsObesityDiabetesPatients
2010
Mining Biomedical Literature for Terms related to Epidemiologic Exposures.
Xu H, Lu Y, Jiang M, Liu M, Denny J, Dai Q, Peterson N. Mining Biomedical Literature for Terms related to Epidemiologic Exposures. AMIA Annual Symposium Proceedings 2010, 2010: 897-901. PMID: 21347108, PMCID: PMC3041399.Peer-Reviewed Original Research