2023
Examining sociodemographic correlates of opioid use, misuse, and use disorders in the All of Us Research Program.
Yeh H, Peltz-Rauchman C, Johnson C, Pawloski P, Chesla D, Waring S, Stevens A, Epstein M, Joseph C, Miller-Matero L, Gui H, Tang A, Boerwinkle E, Cicek M, Clark C, Cohn E, Gebo K, Loperena R, Mayo K, Mockrin S, Ohno-Machado L, Schully S, Ramirez A, Qian J, Ahmedani B. Examining sociodemographic correlates of opioid use, misuse, and use disorders in the All of Us Research Program. PLOS ONE 2023, 18: e0290416. PMID: 37594966, PMCID: PMC10437856, DOI: 10.1371/journal.pone.0290416.Peer-Reviewed Original ResearchConceptsOpioid use disorderOpioid usePrescription opioidsElectronic health recordsReduced oddsDiagnosis of OUDSociodemographic characteristicsPrevalence of OUDNonmedical useLifetime opioid useEHR dataNon-Hispanic white participantsImportant clinical informationNon-medical useLifetime prevalenceStreet opioidsHigher oddsOpioidsClinical informationUse disordersUs Research ProgramSociodemographic correlatesLogistic regressionPrevalenceHealth records
2022
Simplified Machine Learning Models Can Accurately Identify High-Need High-Cost Patients With Inflammatory Bowel Disease
Nguyen N, Patel S, Gabunilas J, Qian A, Cecil A, Jairath V, Sandborn W, Ohno-Machado L, Chen P, Singh S. Simplified Machine Learning Models Can Accurately Identify High-Need High-Cost Patients With Inflammatory Bowel Disease. Clinical And Translational Gastroenterology 2022, 13: e00507. PMID: 35905414, PMCID: PMC10476830, DOI: 10.14309/ctg.0000000000000507.Peer-Reviewed Original ResearchConceptsInflammatory bowel diseaseUnplanned healthcare utilizationAdult patientsBowel diseaseHealthcare utilizationHealthcare costsLogistic regressionRetrospective cohort studyNationwide Readmissions DatabaseIdentification of patientsAdministrative claims dataHigh-cost patientsHNHC patientsCohort studyHospitalized patientsClaims dataHigh riskPatientsTraditional logistic regressionDerivation dataMean AUCIBDMean areaCharacteristic curveDisease
2021
Predictive Analytics for Glaucoma Using Data From the All of Us Research Program
Baxter S, Saseendrakumar B, Paul P, Kim J, Bonomi L, Kuo T, Loperena R, Ratsimbazafy F, Boerwinkle E, Cicek M, Clark C, Cohn E, Gebo K, Mayo K, Mockrin S, Schully S, Ramirez A, Ohno-Machado L, Investigators A. Predictive Analytics for Glaucoma Using Data From the All of Us Research Program. American Journal Of Ophthalmology 2021, 227: 74-86. PMID: 33497675, PMCID: PMC8184631, DOI: 10.1016/j.ajo.2021.01.008.Peer-Reviewed Original ResearchConceptsGlaucoma surgeryPrimary open-angle glaucomaOphthalmic researchSingle-center cohortElectronic health record dataMultivariable logistic regressionSingle-center dataOpen-angle glaucomaHealth record dataMean ageClaims dataUs Research ProgramLogistic regressionSurgeryRecord dataOphthalmic imagingCharacteristic curveExternal validationGlaucomaCohortAUCSingle-center model
2019
Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records
Baxter S, Marks C, Kuo T, Ohno-Machado L, Weinreb R. Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records. American Journal Of Ophthalmology 2019, 208: 30-40. PMID: 31323204, PMCID: PMC6888922, DOI: 10.1016/j.ajo.2019.07.005.Peer-Reviewed Original ResearchConceptsPrimary open-angle glaucomaElectronic health recordsMultivariable logistic regressionSurgical interventionGlaucoma surgeryPOAG patientsSystemic dataHigher mean systolic blood pressureMean systolic blood pressureNon-opioid analgesic medicationsLogistic regressionCertain medication classesEye-specific dataHealth recordsRisk of progressionSystolic blood pressureOpen-angle glaucomaSingle academic institutionAnti-hyperlipidemic medicationsAnalgesic medicationMedication classesProgressive diseaseBlood pressureCalcium blockersOphthalmic medications
2006
PROGNOSIS IN CRITICAL CARE
Ohno-Machado L, Resnic F, Matheny M. PROGNOSIS IN CRITICAL CARE. Annual Review Of Biomedical Engineering 2006, 8: 567-599. PMID: 16834567, DOI: 10.1146/annurev.bioeng.8.061505.095842.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus Statements
2005
Monitoring Device Safety in Interventional Cardiology
Matheny M, Ohno-Machado L, Resnic F. Monitoring Device Safety in Interventional Cardiology. Journal Of The American Medical Informatics Association 2005, 13: 180-187. PMID: 16357356, PMCID: PMC1447549, DOI: 10.1197/jamia.m1908.Peer-Reviewed Original Research
2001
Simplified risk score models accurately predict the risk of major in-hospital complications following percutaneous coronary intervention
Resnic F, Ohno-Machado L, Selwyn A, Simon D, Popma J. Simplified risk score models accurately predict the risk of major in-hospital complications following percutaneous coronary intervention. The American Journal Of Cardiology 2001, 88: 5-9. PMID: 11423050, DOI: 10.1016/s0002-9149(01)01576-4.Peer-Reviewed Original ResearchMeSH KeywordsAngioplasty, Balloon, CoronaryCardiopulmonary BypassCoronary DiseaseFemaleHospital MortalityHumansLogistic ModelsMaleMiddle AgedMyocardial InfarctionNeural Networks, ComputerPostoperative ComplicationsPredictive Value of TestsPrognosisProspective StudiesRisk AssessmentRisk FactorsROC CurveConceptsPercutaneous coronary interventionRisk score modelHospital complicationsCoronary interventionScore modelGlycoprotein IIb/IIIa antagonistsIIb/IIIa antagonistsMultiple logistic regression modelBedside risk stratificationCombined end pointUse of stentsMultiple logistic modelLogistic regression modelsBypass surgeryRisk stratificationSingle centerDecreased riskMyocardial infarctionRisk factorsComplicationsInterventional proceduresEnd pointLogistic regressionLogistic modelROC curve
2000
Major complications after angioplasty in patients with chronic renal failure: a comparison of predictive models.
Lacson R, Ohno-Machado L. Major complications after angioplasty in patients with chronic renal failure: a comparison of predictive models. AMIA Annual Symposium Proceedings 2000, 457-61. PMID: 11079925, PMCID: PMC2243840.Peer-Reviewed Original ResearchConceptsPercutaneous transluminal coronary angioplastyEnd-stage renal diseaseChronic renal failureMajor complicationsRenal failureCongestive heart failurePatient risk factorsTransluminal coronary angioplastyLogistic regression modelsCoronary angioplastyHeart failureRenal diseasePoor outcomeMyocardial infarctionRisk factorsPrior historyPresence of shockComplicationsLogistic regressionPatientsDiscriminatory abilityDemographic characteristicsAngioplastyRegression modelsStandard logistic regression modelDevelopment and evaluation of models to predict death and myocardial infarction following coronary angioplasty and stenting.
Resnic F, Popma J, Ohno-Machado L. Development and evaluation of models to predict death and myocardial infarction following coronary angioplasty and stenting. AMIA Annual Symposium Proceedings 2000, 690-3. PMID: 11079972, PMCID: PMC2243827.Peer-Reviewed Original ResearchConceptsPercutaneous coronary interventionMyocardial infarctionPost-procedural myocardial infarctionMajor acute complicationsRisk-scoring systemRisk of deathMultivariate logistic regressionRisk score modelRisk-scoring modelLogistic regression modelsAcute complicationsCoronary angioplastyCoronary interventionClinical practiceInfarctionScoring systemInterventional cardiologyLogistic regressionRisk modelClinical implementationRisk deathDeathScore modelReasonable discriminationRegression models