2022
Associations Between Vascular Diseases and Alzheimer’s Disease or Related Dementias in a Large Cohort of Men and Women with Colorectal Cancer
Du X, Song L, Schulz P, Xu H, Chan W. Associations Between Vascular Diseases and Alzheimer’s Disease or Related Dementias in a Large Cohort of Men and Women with Colorectal Cancer. Journal Of Alzheimer's Disease 2022, 90: 211-231. PMID: 36093703, PMCID: PMC9661325, DOI: 10.3233/jad-220548.Peer-Reviewed Original ResearchConceptsColorectal cancerVascular diseaseCardiovascular diseaseAlzheimer's diseaseRisk of ADSignificant dose-response relationshipRetrospective cohort studyCohort of patientsTypes of dementiaLong-term riskDose-response relationshipRisk of ADRDTumor factorsCohort studyCumulative incidenceOlder patientsLarge cohortPatientsRelated dementiaHypertensionCancerDiseaseDiabetesDementiaStroke
2015
Effects of Health Insurance on Tumor Stage, Treatment, and Survival in Large Cohorts of Patients with Breast and Colorectal Cancer
Zhang Y, Franzini L, Chan W, Xu H, Du X. Effects of Health Insurance on Tumor Stage, Treatment, and Survival in Large Cohorts of Patients with Breast and Colorectal Cancer. Journal Of Health Care For The Poor And Underserved 2015, 26: 1336-1358. PMID: 26548682, DOI: 10.1353/hpu.2015.0119.Peer-Reviewed Original ResearchConceptsRisk of mortalityColorectal cancerTumor stagePrivate health insuranceCancer patientsHealth insuranceCancer-directed surgeryColorectal cancer patientsTexas Cancer RegistryInsurance coverageAdditional private health insuranceBreast cancer patientsHealth insurance statusHealth insurance coverageOverall survivalCancer RegistryInsurance statusBreast cancerLarge cohortHigh riskMedicare beneficiariesPatientsCancerChemotherapySurgeryTrends and variations in breast and colorectal cancer incidence from 1995 to 2011: A comparative study between Texas Cancer Registry and National Cancer Institute’s Surveillance, Epidemiology and End Results data
LIU Z, ZHANG Y, FRANZIN L, CORMIER J, CHAN W, XU H, DU X. Trends and variations in breast and colorectal cancer incidence from 1995 to 2011: A comparative study between Texas Cancer Registry and National Cancer Institute’s Surveillance, Epidemiology and End Results data. International Journal Of Oncology 2015, 46: 1819-1826. PMID: 25672365, PMCID: PMC4356494, DOI: 10.3892/ijo.2015.2881.Peer-Reviewed Original ResearchConceptsColorectal cancer incidenceNational Cancer Institute's SurveillanceTexas Cancer RegistryBreast cancer incidenceCancer incidenceCancer RegistryAge-adjusted breast cancer incidenceColorectal cancer patientsEnd Results (SEER) dataSEER areasColorectal cancerCancer patientsIncidence rateRelative riskIncidenceBreastRegistrySurveillanceEpidemiologySEERResult dataTemporal trendsEnd resultPatientsParallel comparisonColorectal cancer drug target prediction using ontology-based inference and network analysis
Tao C, Sun J, Zheng W, Chen J, Xu H. Colorectal cancer drug target prediction using ontology-based inference and network analysis. Database 2015, 2015: bav015. PMID: 25818893, PMCID: PMC4375358, DOI: 10.1093/database/bav015.Peer-Reviewed Original ResearchConceptsColorectal cancer
2013
Applying active learning to high-throughput phenotyping algorithms for electronic health records data
Chen Y, Carroll R, Hinz E, Shah A, Eyler A, Denny J, Xu H. Applying active learning to high-throughput phenotyping algorithms for electronic health records data. Journal Of The American Medical Informatics Association 2013, 20: e253-e259. PMID: 23851443, PMCID: PMC3861916, DOI: 10.1136/amiajnl-2013-001945.Peer-Reviewed Original ResearchConceptsActive learningUnrefined featuresSupervised Machine Learning AlgorithmsRefined featuresPhenotyping algorithmElectronic health record dataMachine Learning AlgorithmsHealth record dataVenous thromboembolismRheumatoid arthritisFeature engineeringDomain expertsDomain knowledgePhenotyping tasksLearning algorithmFeature setsLearning approachColorectal cancerAL approachCurve scorePassive learning approachHigh-throughput phenotyping methodsAlgorithmSmall setRecord data
2011
Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases.
Xu H, Fu Z, Shah A, Chen Y, Peterson N, Chen Q, Mani S, Levy M, Dai Q, Denny J. Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases. AMIA Annual Symposium Proceedings 2011, 2011: 1564-72. PMID: 22195222, PMCID: PMC3243156.Peer-Reviewed Original ResearchConceptsEntire electronic health recordElectronic health recordsNatural language processingHealth recordsStructured EHR dataMachine learningText dataNarrative text dataF-measureLanguage processingClinical narrativesEHR dataSuch tasksColorectal cancerDetection methodConcept identificationCohort of patientsColorectal cancer casesVanderbilt University HospitalCase detection methodsClinical notesCRC patientsCRC casesUniversity HospitalCancer cases