2018
A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set
Rasmy L, Wu Y, Wang N, Geng X, Zheng W, Wang F, Wu H, Xu H, Zhi D. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. Journal Of Biomedical Informatics 2018, 84: 11-16. PMID: 29908902, PMCID: PMC6076336, DOI: 10.1016/j.jbi.2018.06.011.Peer-Reviewed Original ResearchConceptsRecurrent neural networkOnset riskCapability of RNNCerner Health FactsHeterogeneous EHR dataHeart failure patientsData setsElectronic health record dataDeep learning modelsDifferent patient populationsNeural network-based predictive modelDifferent patient groupsHealth record dataEHR data setsPredictive modelingSmall data setsFailure patientsPatient groupPatient populationReduction of AUCNeural networkRNN modelRETAIN modelHealth FactsHospital
2017
Literature-Based Discovery of Confounding in Observational Clinical Data.
Malec S, Wei P, Xu H, Bernstam E, Myneni S, Cohen T. Literature-Based Discovery of Confounding in Observational Clinical Data. AMIA Annual Symposium Proceedings 2017, 2016: 1920-1929. PMID: 28269951, PMCID: PMC5333204.Peer-Reviewed Original Research
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 students
2012
Large-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 stages