2023
AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models
Datta S, Lee K, Paek H, Manion F, Ofoegbu N, Du J, Li Y, Huang L, Wang J, Lin B, Xu H, Wang X. AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models. Journal Of The American Medical Informatics Association 2023, 31: 375-385. PMID: 37952206, PMCID: PMC10797270, DOI: 10.1093/jamia/ocad218.Peer-Reviewed Original ResearchConceptsLanguage modelInformation extraction systemOverall F1 scoreCriteria informationF1 scoreManual annotationScalable solutionContextual informationComplex scenariosContextual attributesExtraction systemReal-world settingsSystem evaluationModeling capabilitiesClinical trial protocol documentsInformationProtocol documents
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 dementiaHypertensionCancerDiseaseDiabetesDementiaStrokeRisk 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 diagnosisWomenIncidenceRiskAnalysis of Dual Combination Therapies Used in Treatment of Hypertension in a Multinational Cohort
Lu Y, Van Zandt M, Liu Y, Li J, Wang X, Chen Y, Chen Z, Cho J, Dorajoo SR, Feng M, Hsu MH, Hsu JC, Iqbal U, Jonnagaddala J, Li YC, Liaw ST, Lim HS, Ngiam KY, Nguyen PA, Park RW, Pratt N, Reich C, Rhee SY, Sathappan SMK, Shin SJ, Tan HX, You SC, Zhang X, Krumholz HM, Suchard MA, Xu H. Analysis of Dual Combination Therapies Used in Treatment of Hypertension in a Multinational Cohort. JAMA Network Open 2022, 5: e223877. PMID: 35323951, PMCID: PMC8948532, DOI: 10.1001/jamanetworkopen.2022.3877.Peer-Reviewed Original ResearchConceptsDual combination therapyUse of ACEIAntihypertensive drug classesProportion of patientsKhoo Teck Puat HospitalCombination therapyUniversity Hospital databaseHospital databaseDrug classesDual combinationSouth Western Sydney Local Health DistrictWestern Sydney Local Health DistrictPatients age 65 yearsSydney Local Health DistrictElectronic health record databasePatients age 18Local Health DistrictAge 65 yearsTreatment of hypertensionHealth record databaseARB monotherapyTreatment escalationAdult patientsCohort studyCombination regimen
2021
Comprehensive Characterization of COVID-19 Patients with Repeatedly Positive SARS-CoV-2 Tests Using a Large U.S. Electronic Health Record Database
Dong X, Zhou Y, Shu X, Bernstam E, Stern R, Aronoff D, Xu H, Lipworth L. Comprehensive Characterization of COVID-19 Patients with Repeatedly Positive SARS-CoV-2 Tests Using a Large U.S. Electronic Health Record Database. Microbiology Spectrum 2021, 9: 10.1128/spectrum.00327-21. PMID: 34406805, PMCID: PMC8552669, DOI: 10.1128/spectrum.00327-21.Peer-Reviewed Original ResearchConceptsPositive SARS-CoV-2 testSARS-CoV-2 testSecond positive testElectronic health record databaseCases of reinfectionHealth record databasePositive testPositive SARS-CoV-2 PCR test resultsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testingSARS-CoV-2 PCR test resultsRecord databaseSevere acute respiratory syndrome coronavirus 2Intensive care unit admissionAcute respiratory syndrome coronavirus 2SARS-CoV-2 infectionRespiratory syndrome coronavirus 2Long-term health consequencesLarge electronic health record databasePotential long-term health consequencesCare unit admissionOverweight/obeseChronic medical conditionsPositive molecular testCOVID-19 patientsSyndrome coronavirus 2Privacy-protecting, reliable response data discovery using COVID-19 patient observations
Kim J, Neumann L, Paul P, Day M, Aratow M, Bell D, Doctor J, Hinske L, Jiang X, Kim K, Matheny M, Meeker D, Pletcher M, Schilling L, SooHoo S, Xu H, Zheng K, Ohno-Machado L, Anderson D, Anderson N, Balacha C, Bath T, Baxter S, Becker-Pennrich A, Bernstam E, Carter W, Chau N, Choi Y, Covington S, DuVall S, El-Kareh R, Florian R, Follett R, Geisler B, Ghigi A, Gottlieb A, Hu Z, Ir D, Knight T, Koola J, Kuo T, Lee N, Mansmann U, Mou Z, Murphy R, Neumann L, Nguyen N, Niedermayer S, Park E, Perkins A, Post K, Rieder C, Scherer C, Soares A, Soysal E, Tep B, Toy B, Wang B, Wu Z, Zhou Y, Zucker R. Privacy-protecting, reliable response data discovery using COVID-19 patient observations. Journal Of The American Medical Informatics Association 2021, 28: 1765-1776. PMID: 34051088, PMCID: PMC8194878, DOI: 10.1093/jamia/ocab054.Peer-Reviewed Original Research
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-9Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study
Xiang Y, Ji H, Zhou Y, Li F, Du J, Rasmy L, Wu S, Zheng W, Xu H, Zhi D, Zhang Y, Tao C. Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study. Journal Of Medical Internet Research 2020, 22: e16981. PMID: 32735224, PMCID: PMC7428917, DOI: 10.2196/16981.Peer-Reviewed Original ResearchConceptsAttentive Neural NetworkAsthma exacerbationsRisk factorsNeural networkAdvanced deep learning modelsClinical variablesDeep learning modelsCerner Health Facts databaseLarge electronic health recordNeural network modelRetrospective cohort studyHealth Facts databasePotential risk factorsRisk factor analysisPersonalized risk factorsElectronic health recordsBaseline methodsLearning modelPersonalized risk scoreProgressive asthmaAsthma symptomsEsophageal refluxAdult patientsCohort studyTime-SensitiveLearning from local to global: An efficient distributed algorithm for modeling time-to-event data
Duan R, Luo C, Schuemie M, Tong J, Liang C, Chang H, Boland M, Bian J, Xu H, Holmes J, Forrest C, Morton S, Berlin J, Moore J, Mahoney K, Chen Y. Learning from local to global: An efficient distributed algorithm for modeling time-to-event data. Journal Of The American Medical Informatics Association 2020, 27: 1028-1036. PMID: 32626900, PMCID: PMC7647322, DOI: 10.1093/jamia/ocaa044.Peer-Reviewed Original Research
2019
Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm
Duan R, Boland M, Liu Z, Liu Y, Chang H, Xu H, Chu H, Schmid C, Forrest C, Holmes J, Schuemie M, Berlin J, Moore J, Chen Y. Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm. Journal Of The American Medical Informatics Association 2019, 27: 376-385. PMID: 31816040, PMCID: PMC7025371, DOI: 10.1093/jamia/ocz199.Peer-Reviewed Original ResearchAnalysis on geographic variations in hospital deaths and endovascular therapy in ischaemic stroke patients: an observational cross-sectional study in China
Chen H, Shi L, Wang N, Han Y, Lin Y, Dai M, Liu H, Dong X, Xue M, Xu H. Analysis on geographic variations in hospital deaths and endovascular therapy in ischaemic stroke patients: an observational cross-sectional study in China. BMJ Open 2019, 9: e029079. PMID: 31239305, PMCID: PMC6597735, DOI: 10.1136/bmjopen-2019-029079.Peer-Reviewed Original ResearchConceptsObservational cross-sectional studyIschemic stroke patientsCross-sectional studyEVT useHospital mortalityTertiary hospitalStroke patientsEmergency departmentAssociated potential risk factorsNationwide hospital discharge databaseEndovascular therapy useHigher hospital mortalityHospital mortality rateHospital discharge databaseHospital discharge dataPost-stroke outcomesChina's tertiary hospitalsPotential risk factorsCause of deathNational Health CommissionHospital deathHospitalised patientsOlder patientsEndovascular therapyMale patientsParsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison
Zhang Y, Tiryaki F, Jiang M, Xu H. Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison. BMC Medical Informatics And Decision Making 2019, 19: 77. PMID: 30943955, PMCID: PMC6448179, DOI: 10.1186/s12911-019-0783-2.Peer-Reviewed Original Research
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 systemAssociation of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin
Vashisht R, Jung K, Schuler A, Banda J, Park R, Jin S, Li L, Dudley J, Johnson K, Shervey M, Xu H, Wu Y, Natrajan K, Hripcsak G, Jin P, Van Zandt M, Reckard A, Reich C, Weaver J, Schuemie M, Ryan P, Callahan A, Shah N. Association of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin. JAMA Network Open 2018, 1: e181755. PMID: 30646124, PMCID: PMC6324274, DOI: 10.1001/jamanetworkopen.2018.1755.Peer-Reviewed Original ResearchConceptsDPP-4 inhibitorsDipeptidyl peptidase-4 inhibitorsFirst-line therapyPeptidase-4 inhibitorsSecond-line drugsType 2 diabetesMyocardial infarctionEye disordersKidney disordersDrug classesSecond-line treatment choiceTotal hemoglobinObservational Health Data SciencesSecond-line treatment optionNew-user cohort studyEffectiveness of sulfonylureasSecond-line treatmentHemoglobin A1c levelsUse of sulfonylureasHealth Data SciencesLarge international studyElectronic medical recordsRoutine medical practiceInsurance claims dataCohort studyA study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set
Rasmy L, Wu Y, Wang N, Geng X, Zheng W, Wang F, Wu H, Xu H, Zhi D. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. Journal Of Biomedical Informatics 2018, 84: 11-16. PMID: 29908902, PMCID: PMC6076336, DOI: 10.1016/j.jbi.2018.06.011.Peer-Reviewed Original ResearchConceptsRecurrent neural networkOnset riskCapability of RNNCerner Health FactsHeterogeneous EHR dataHeart failure patientsData setsElectronic health record dataDeep learning modelsDifferent patient populationsNeural network-based predictive modelDifferent patient groupsHealth record dataEHR data setsPredictive modelingSmall data setsFailure patientsPatient groupPatient populationReduction of AUCNeural networkRNN modelRETAIN modelHealth FactsHospital
2017
Risk of angioedema associated with levetiracetam compared with phenytoin: Findings of the observational health data sciences and informatics research network
Duke J, Ryan P, Suchard M, Hripcsak G, Jin P, Reich C, Schwalm M, Khoma Y, Wu Y, Xu H, Shah N, Banda J, Schuemie M. Risk of angioedema associated with levetiracetam compared with phenytoin: Findings of the observational health data sciences and informatics research network. Epilepsia 2017, 58: e101-e106. PMID: 28681416, PMCID: PMC6632067, DOI: 10.1111/epi.13828.Peer-Reviewed Original ResearchConceptsAngioedema riskAngioedema eventsHazard ratioObservational Health Data SciencesNew-user cohort studySummary hazard ratioRisk of angioedemaHealth Data SciencesAdverse event reportsPhenytoin usersResearch NetworkPhenytoin groupCohort studyTreat analysisAntiepileptic drugsComparator groupSeizure patientsLower riskLevetiracetamAngioedemaFurther studiesEvent reportsSignificant increaseRiskPhenytoinEvaluating the role of race and medication in protection of uterine fibroids by type 2 diabetes exposure
Velez Edwards D, Hartmann K, Wellons M, Shah A, Xu H, Edwards T. Evaluating the role of race and medication in protection of uterine fibroids by type 2 diabetes exposure. BMC Women's Health 2017, 17: 28. PMID: 28399866, PMCID: PMC5387248, DOI: 10.1186/s12905-017-0386-y.Peer-Reviewed Original ResearchConceptsT2D medicationsUF riskAnnual healthcare costsElectronic medical record algorithmCase-control studyLarge clinical populationFurther mechanistic researchBackgroundUterine fibroidsT2D diagnosisDiabetes exposureMedication typeDiabetes presenceInsulin usersUterine fibroidsInsulin treatmentProtective associationStratified analysisClinical cohortConclusionsThese dataProtective effectLarge cohortCase-control statusHealthcare costsT2DMedications
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 beneficiariesPatientsCancerChemotherapySurgeryClassification of Cancer Primary Sites Using Machine Learning and Somatic Mutations
Chen Y, Sun J, Huang L, Xu H, Zhao Z. Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations. BioMed Research International 2015, 2015: 491502. PMID: 26539502, PMCID: PMC4619847, DOI: 10.1155/2015/491502.Peer-Reviewed Original ResearchConceptsMachine learningF-measureAvailable big dataSupport vector machineBig dataVector machineClassification experimentsAccurate classificationCancer classificationGene function informationMachineSomatic mutation informationClassificationMutation informationFunction informationLearningGene symbolsInformationGene featuresGreat opportunityPerformanceSomatic mutation dataMutation dataAccuracyPredictionIdentifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2
Stubbs A, Kotfila C, Xu H, Uzuner Ö. Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2. Journal Of Biomedical Informatics 2015, 58: s67-s77. PMID: 26210362, PMCID: PMC4978189, DOI: 10.1016/j.jbi.2015.07.001.Peer-Reviewed Original ResearchMeSH KeywordsAgedBostonCohort StudiesComorbidityComputer SecurityConfidentialityCoronary Artery DiseaseData MiningDiabetes ComplicationsElectronic Health RecordsFemaleHumansIncidenceLongitudinal StudiesMaleMiddle AgedNarrationNatural Language ProcessingPattern Recognition, AutomatedRisk AssessmentVocabulary, ControlledConceptsCoronary artery diseaseRisk factorsLongitudinal medical recordsMedical recordsMedical risk factorsArtery diseaseDiabetic patientsSmoking statusHeart diseaseFamily historyI2b2/UTHealth natural language processingDiseaseI2b2/UTHealthProgressionUTHealthHypertensionHyperlipidemiaFactorsObesityDiabetesPatients