2024
SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials
Lee K, Paek H, Huang L, Hilton C, Datta S, Higashi J, Ofoegbu N, Wang J, Rubinstein S, Cowan A, Kwok M, Warner J, Xu H, Wang X. SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials. Informatics In Medicine Unlocked 2024, 50: 101589. PMID: 39493413, PMCID: PMC11530223, DOI: 10.1016/j.imu.2024.101589.Peer-Reviewed Original ResearchAntibody-drug conjugatesOverall response rateMultiple myelomaF1 scoreCAR-TComplete responseBispecific antibodiesComparative performance analysisClinical trial studyClinical trial outcomesLanguage modelAccurate data extractionTherapy subgroupFine granularityOncology clinical trialsAdverse eventsClinical decision-makingPerformance analysisClinical trialsInnovative therapiesDiverse therapiesClinical trial abstractsCancer domainData elementsTherapyUnveiling consistency: A large-scale analysis of conference proceedings and subsequent publications in oncology clinical trials using large language models.
Lee K, Paek H, Huang L, Datta S, Annan A, Ofoegbu N, Higashi M, Hilton C, Rubinstein S, Cowan A, Kwok M, Warner J, Xu H, Wang X. Unveiling consistency: A large-scale analysis of conference proceedings and subsequent publications in oncology clinical trials using large language models. Journal Of Clinical Oncology 2024, 42: 7568-7568. DOI: 10.1200/jco.2024.42.16_suppl.7568.Peer-Reviewed Original ResearchAmerican Society of Clinical Oncology conferencesConference abstractsOncology clinical trialsStudies due to variationsEfficacy outcomesP-valueTwo-proportion z-testClinical trialsMinimal residual disease negativityLung cancer studiesPublished articlesTrial abstractsGood partial responseCohort sizeCytokine release syndromeOutcome valuesFollow-up timeOverall response rateTrial findingsClinical trial findingsTime pointsCancer studiesClinical decisionsDiverse cancer typesComplete response
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
2014
Adapting a natural language processing tool to facilitate clinical trial curation for personalized cancer therapy.
Zeng J, Wu Y, Bailey A, Johnson A, Holla V, Bernstam E, Xu H, Meric-Bernstam F. Adapting a natural language processing tool to facilitate clinical trial curation for personalized cancer therapy. AMIA Joint Summits On Translational Science Proceedings 2014, 2014: 126-31. PMID: 25717412, PMCID: PMC4333699.Peer-Reviewed Original Research
2012
Data mining methodologies for pharmacovigilance
Liu M, Matheny M, Hu Y, Xu H. Data mining methodologies for pharmacovigilance. ACM SIGKDD Explorations Newsletter 2012, 14: 35-42. DOI: 10.1145/2408736.2408742.Peer-Reviewed Original ResearchAdverse drug reactionsElectronic medical recordsLong-term adverse drug reactionsTerm adverse drug reactionPrevention of ADRsAdverse drug eventsPatient-reported dataPotential adverse drug reactionsNational surveillance systemEmergency departmentDrug eventsDrug reactionsPreclinical dataMedical recordsADR monitoringClinical trialsMedication safetyPreclinical characteristicsSpontaneous reportsPostmarketing phaseOnline health forumsPostmarketing stageDrug developmentHealth forumsPre-marketing stagesLarge-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs
Liu M, Wu Y, Chen Y, Sun J, Zhao Z, Chen X, Matheny M, Xu H. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs. Journal Of The American Medical Informatics Association 2012, 19: e28-e35. PMID: 22718037, PMCID: PMC3392844, DOI: 10.1136/amiajnl-2011-000699.Peer-Reviewed Original ResearchConceptsAdverse drug reactionsPost-marketing phaseDrug reactionsSevere adverse drug reactionsImportant adverse drug reactionsWithdrawal of rofecoxibPotential adverse drug reactionsPost-marketing surveillanceADR predictionPatient morbidityClinical trialsMajor causeLarge-scale studiesDrugsMolecular pathwaysDrug developmentPhenotypic featuresSignificant improvementPhenotypic characteristicsEarly stages