2022
Risk of Developing Alzheimer’s Disease and Related Dementias in Association with Cardiovascular Disease, Stroke, Hypertension, and Diabetes in a Large Cohort of Women with Breast Cancer and with up to 26 Years of Follow-Up
Du X, Song L, Schulz P, Xu H, Chan W. Risk of Developing Alzheimer’s Disease and Related Dementias in Association with Cardiovascular Disease, Stroke, Hypertension, and Diabetes in a Large Cohort of Women with Breast Cancer and with up to 26 Years of Follow-Up. Journal Of Alzheimer's Disease 2022, 87: 415-432. PMID: 35311707, PMCID: PMC9117151, DOI: 10.3233/jad-215657.Peer-Reviewed Original ResearchConceptsCardiovascular diseaseBreast cancerAsian/Pacific IslandersHigh riskAlzheimer's diseaseRisk of ADRDCumulative incidenceWhite womenLong-term incidencePacific IslandersBlack womenVascular diseaseHypertensionLarge cohortLower riskDiabetesRelated dementiaStrokeCancerDiseaseADRDCancer diagnosisWomenIncidenceRisk
2018
Extraction of BI-RADS findings from breast ultrasound reports in Chinese using deep learning approaches
Miao S, Xu T, Wu Y, Xie H, Wang J, Jing S, Zhang Y, Zhang X, Yang Y, Zhang X, Shan T, Wang L, Xu H, Wang S, Liu Y. Extraction of BI-RADS findings from breast ultrasound reports in Chinese using deep learning approaches. International Journal Of Medical Informatics 2018, 119: 17-21. PMID: 30342682, DOI: 10.1016/j.ijmedinf.2018.08.009.Peer-Reviewed Original ResearchConceptsLearning-based methodsBreast ultrasound reportsElectronic health record systemsTraditional machine learning-based methodsDeep learning-based approachDeep learning-based methodsNatural language processing methodsMachine learning-based methodsDeep learning technologyConditional random field algorithmDeep learning approachLanguage processing methodsLearning-based approachUltrasound reportsBreast cancer researchRule-based methodHealth record systemsBreast radiology reportsLearning technologyNLP approachLearning approachField algorithmDetailed clinical informationWide adoptionRecord system
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 comparison
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
2004
Facilitating cancer research using natural language processing of pathology reports.
Xu H, Anderson K, Grann V, Friedman C. Facilitating cancer research using natural language processing of pathology reports. 2004, 107: 565-72. PMID: 15360876.Peer-Reviewed Original Research