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
Analysis on geographic variations in hospital deaths and endovascular therapy in ischaemic stroke patients: an observational cross-sectional study in China
Chen H, Shi L, Wang N, Han Y, Lin Y, Dai M, Liu H, Dong X, Xue M, Xu H. Analysis on geographic variations in hospital deaths and endovascular therapy in ischaemic stroke patients: an observational cross-sectional study in China. BMJ Open 2019, 9: e029079. PMID: 31239305, PMCID: PMC6597735, DOI: 10.1136/bmjopen-2019-029079.Peer-Reviewed Original ResearchConceptsObservational cross-sectional studyIschemic stroke patientsCross-sectional studyEVT useHospital mortalityTertiary hospitalStroke patientsEmergency departmentAssociated potential risk factorsNationwide hospital discharge databaseEndovascular therapy useHigher hospital mortalityHospital mortality rateHospital discharge databaseHospital discharge dataPost-stroke outcomesChina's tertiary hospitalsPotential risk factorsCause of deathNational Health CommissionHospital deathHospitalised patientsOlder patientsEndovascular therapyMale patients