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
2017
Accurate Identification of Fatty Liver Disease in Data Warehouse Utilizing Natural Language Processing
Redman J, Natarajan Y, Hou J, Wang J, Hanif M, Feng H, Kramer J, Desiderio R, Xu H, El-Serag H, Kanwal F. Accurate Identification of Fatty Liver Disease in Data Warehouse Utilizing Natural Language Processing. Digestive Diseases And Sciences 2017, 62: 2713-2718. PMID: 28861720, DOI: 10.1007/s10620-017-4721-9.Peer-Reviewed Original ResearchConceptsData warehouseFatty liver diseaseLanguage processingNatural language processingLiver diseaseF-measureAlgorithm developmentVeterans Affairs Corporate Data WarehouseMagnetic resonance imaging reportsOutcomes of patientsAlgorithmExpert radiologistsValidation methodElectronic medical recordsCorporate Data WarehouseWarehouseAbdominal ultrasoundManual reviewHepatic steatosisMedical recordsRandom national sampleClinical studiesLarge cohortComputerized tomographyImaging reports
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