2019
Discovery 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
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
2011
Predicting Clopidogrel Response Using DNA Samples Linked to an Electronic Health Record
Delaney J, Ramirez A, Bowton E, Pulley J, Basford M, Schildcrout J, Shi Y, Zink R, Oetjens M, Xu H, Cleator J, Jahangir E, Ritchie M, Masys D, Roden D, Crawford D, Denny J. Predicting Clopidogrel Response Using DNA Samples Linked to an Electronic Health Record. Clinical Pharmacology & Therapeutics 2011, 91: 257-263. PMID: 22190063, PMCID: PMC3621954, DOI: 10.1038/clpt.2011.221.Peer-Reviewed Original ResearchMeSH KeywordsAgedAryl Hydrocarbon HydroxylasesAryldialkylphosphataseATP Binding Cassette Transporter, Subfamily BATP Binding Cassette Transporter, Subfamily B, Member 1ClopidogrelCytochrome P-450 CYP2C19Databases, Nucleic AcidElectronic Health RecordsFemaleGenotypeHumansMaleMyocardial InfarctionPharmacogeneticsPlatelet Aggregation InhibitorsPolymorphism, GeneticStentsThrombosisTiclopidineTreatment OutcomeConceptsPercutaneous coronary interventionElectronic health recordsCardiac eventsMyocardial infarctionRecurrent cardiac eventsRecurrent cardiovascular eventsHealth recordsUse of EHRsClopidogrel therapyCardiovascular eventsClopidogrel treatmentClopidogrel resistanceClopidogrel responseCoronary interventionStent thrombosisReal-world settingCYP2C19ABCB1PON1Pharmacogenomic studiesRecurrent eventsTreatmentDNA repositoryDNA samplesInfarction