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-9
2014
Identifying plausible adverse drug reactions using knowledge extracted from the literature
Shang N, Xu H, Rindflesch T, Cohen T. Identifying plausible adverse drug reactions using knowledge extracted from the literature. Journal Of Biomedical Informatics 2014, 52: 293-310. PMID: 25046831, PMCID: PMC4261011, DOI: 10.1016/j.jbi.2014.07.011.Peer-Reviewed Original ResearchConceptsPredication-based Semantic IndexingReflective Random IndexingLBD methodsNatural language processing toolsBiomedical literatureDrug-adverse event associationsLanguage processing toolsSemantic indexingElectronic health recordsRandom IndexingHuman reviewVast repositoryDiscovery methodsVolume of knowledgeProcessing toolsEvaluation setHealth recordsData sourcesEvent associationsIndexingDrug-effect relationshipsRepositoryLarge volumesADR associationsReasoning pathways
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 stagesPortability of an algorithm to identify rheumatoid arthritis in electronic health records
Carroll R, Thompson W, Eyler A, Mandelin A, Cai T, Zink R, Pacheco J, Boomershine C, Lasko T, Xu H, Karlson E, Perez R, Gainer V, Murphy S, Ruderman E, Pope R, Plenge R, Kho A, Liao K, Denny J. Portability of an algorithm to identify rheumatoid arthritis in electronic health records. Journal Of The American Medical Informatics Association 2012, 19: e162-e169. PMID: 22374935, PMCID: PMC3392871, DOI: 10.1136/amiajnl-2011-000583.Peer-Reviewed Original Research