2021
Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review
Nguyen N, Picetti D, Dulai P, Jairath V, Sandborn W, Ohno-Machado L, Chen P, Singh S. Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review. Journal Of Crohn's And Colitis 2021, 16: 398-413. PMID: 34492100, PMCID: PMC8919806, DOI: 10.1093/ecco-jcc/jjab155.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsInflammatory bowel diseaseBowel diseaseClinical dataHigh riskRisk predictionSystematic reviewAcute severe ulcerative colitisLongitudinal disease activitySevere ulcerative colitisAdverse clinical outcomesBias assessment toolRisk of biasAvailable clinical dataMachine learning-based prediction modelsPrediction model RiskDisease activityCohort studyUlcerative colitisClinical outcomesTreatment responseClinical applicabilityLearning-based prediction modelsExternal validationPatientsRisk
2015
The differential associations of preexisting conditions with trauma-related outcomes in the presence of competing risks
Calvo R, Lindsay S, Edland S, Macera C, Wingard D, Ohno-Machado L, Sise M. The differential associations of preexisting conditions with trauma-related outcomes in the presence of competing risks. Injury 2015, 47: 677-684. PMID: 26684173, DOI: 10.1016/j.injury.2015.10.055.Peer-Reviewed Original ResearchConceptsPre-existing chronic conditionsOlder trauma patientsHospital mortalityTrauma patientsMortality riskHigh-risk groupLow-risk subgroupsLower mortality riskLonger HLOSHospital lengthMost patientsRisk patientsClinical outcomesLiver diseaseRetrospective studyTrauma populationTrauma-related outcomesRisk regressionChronic conditionsExcess mortalityParkinson's diseaseLevel ICare facilitiesPatientsClinical decision
2004
Exploration of a Bayesian Updating Methodology to Monitor the Safety of Interventional Cardiovascular Procedures
Resnic F, Zou K, Do D, Apostolakis G, Ohno-Machado L. Exploration of a Bayesian Updating Methodology to Monitor the Safety of Interventional Cardiovascular Procedures. Medical Decision Making 2004, 24: 399-407. PMID: 15271278, DOI: 10.1177/0272989x04267012.Peer-Reviewed Original ResearchConceptsConventional statistical methodsPrior probability distributionProbability distributionStatistical methodsBayesian updatingMedical device safety evaluationStable estimatesPatient risk groupsInterventional cardiology proceduresInterventional cardiovascular proceduresBayesianClinical outcomesPatient groupRotational atherectomyRisk groupsCardiovascular proceduresClinical practiceMethodologyCardiology proceduresEstimationEvent ratesSafety experienceEstimation of riskFrameworkNovel approach
2003
No-reflow is an independent predictor of death and myocardial infarction after percutaneous coronary intervention
Resnic F, Wainstein M, Lee M, Behrendt D, Wainstein R, Ohno-Machado L, Kirshenbaum J, Rogers C, Popma J, Piana R. No-reflow is an independent predictor of death and myocardial infarction after percutaneous coronary intervention. American Heart Journal 2003, 145: 42-46. PMID: 12514653, DOI: 10.1067/mhj.2003.36.Peer-Reviewed Original ResearchConceptsPercutaneous coronary interventionPostprocedural myocardial infarctionStrong independent predictorMyocardial infarctionIndependent predictorsSodium nitroprussideInhospital outcomesCoronary interventionClinical outcomesSaphenous vein graft interventionIntracoronary vasodilator therapyVein graft interventionAdministration of verapamilAcute myocardial infarctionRate of deathInhospital mortalityVasodilator therapyCardiogenic shockBaseline demographicsGraft interventionUnstable anginaAdverse eventsConsecutive patientsIntracoronary verapamilInfarction