Featured Publications
Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials
Oikonomou EK, Spatz ES, Suchard MA, Khera R. Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials. The Lancet Digital Health 2022, 4: e796-e805. PMID: 36307193, PMCID: PMC9768739, DOI: 10.1016/s2589-7500(22)00170-4.Peer-Reviewed Original ResearchConceptsSystolic blood pressure controlBlood pressure controlIntensive systolic blood pressure controlType 2 diabetesPressure controlCardiovascular benefitsClinical trialsMajor adverse cardiovascular eventsFirst major adverse cardiovascular eventLarge randomised clinical trialsACCORD-BP trialAdverse cardiovascular eventsRandomised clinical trialsSystolic blood pressureCox regression analysisTreatment effectsHazard ratio estimatesACCORD-BPBP trialCardiovascular eventsBlood pressurePrimary outcomeStandard treatmentBaseline variablesIndex patients
2023
Accelerating chest pain evaluation with machine learning
Thangaraj P, Khera R. Accelerating chest pain evaluation with machine learning. European Heart Journal Acute Cardiovascular Care 2023, 12: 753-754. PMID: 37793075, PMCID: PMC11004857, DOI: 10.1093/ehjacc/zuad117.Peer-Reviewed Original ResearchMachine learning in precision diabetes care and cardiovascular risk prediction
Oikonomou E, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovascular Diabetology 2023, 22: 259. PMID: 37749579, PMCID: PMC10521578, DOI: 10.1186/s12933-023-01985-3.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMeSH KeywordsArtificial IntelligenceCardiovascular DiseasesDiabetes MellitusHeart Disease Risk FactorsHumansMachine LearningRisk FactorsConceptsArtificial intelligence solutionsArtificial intelligence productsData-driven methodIntelligence solutionsArtificial intelligenceMachine learningPersonalized solutionsIntelligence productsBias mitigationMachineKey issuesPredictive modelSuch modelsSuccessful applicationRisk predictionParadigm shiftIntelligenceKey propertiesApplicationsLearningPersonalized careFrameworkSolutionCurrent regulatory frameworkHealthcareClinical Risk Prediction Models with Meta-Learning Prototypes of Patient Heterogeneity
Zhang L, Khera R, Mortazavi B. Clinical Risk Prediction Models with Meta-Learning Prototypes of Patient Heterogeneity. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083199, PMCID: PMC11007255, DOI: 10.1109/embc40787.2023.10340765.Peer-Reviewed Original ResearchNonexercise machine learning models for maximal oxygen uptake prediction in national population surveys.
Liu Y, Herrin J, Huang C, Khera R, Dhingra L, Dong W, Mortazavi B, Krumholz H, Lu Y. Nonexercise machine learning models for maximal oxygen uptake prediction in national population surveys. Journal Of The American Medical Informatics Association 2023, 30: 943-952. PMID: 36905605, PMCID: PMC10114129, DOI: 10.1093/jamia/ocad035.Peer-Reviewed Original Research
2021
Assessing Performance of Machine Learning—Reply
Khera R, Mortazavi BJ, Krumholz HM. Assessing Performance of Machine Learning—Reply. JAMA Cardiology 2021, 6: 1466-1466. PMID: 34586344, DOI: 10.1001/jamacardio.2021.3715.Commentaries, Editorials and LettersUse of Machine Learning Models to Predict Death After Acute Myocardial Infarction
Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N, Rumsfeld JS, Masoudi FA, Huang C, Normand SL, Mortazavi BJ, Krumholz HM. Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction. JAMA Cardiology 2021, 6: 633-641. PMID: 33688915, PMCID: PMC7948114, DOI: 10.1001/jamacardio.2021.0122.Peer-Reviewed Original ResearchMeSH KeywordsAgedCohort StudiesFemaleHospital MortalityHumansMachine LearningMaleMyocardial InfarctionRegistriesRisk AssessmentUnited StatesConceptsMachine learning modelsMeta-classifier modelLearning modelNeural networkGradient descent boostingAcute myocardial infarctionContemporary machineGradient descentXGBoost modelXGBoostHospital mortalityCohort studyLogistic regressionMyocardial infarctionNetworkChest Pain-MI RegistryPrecise classificationIndependent validation dataInitial laboratory valuesNovel methodLarge national registryHigh-risk individualsData analysisValidation dataResolution of risk
2020
Digital Phenotyping of Myocardial Dysfunction With 12-Lead ECG Tiptoeing Into the Future With Machine Learning ∗
Nasir K, Khera R. Digital Phenotyping of Myocardial Dysfunction With 12-Lead ECG Tiptoeing Into the Future With Machine Learning ∗. Journal Of The American College Of Cardiology 2020, 76: 942-944. PMID: 32819468, DOI: 10.1016/j.jacc.2020.07.001.Commentaries, Editorials and LettersConceptsDigital phenotyping
2019
Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction
Angraal S, Mortazavi BJ, Gupta A, Khera R, Ahmad T, Desai NR, Jacoby DL, Masoudi FA, Spertus JA, Krumholz HM. Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction. JACC Heart Failure 2019, 8: 12-21. PMID: 31606361, DOI: 10.1016/j.jchf.2019.06.013.Peer-Reviewed Original ResearchConceptsHF hospitalizationRisk of mortalityEjection fractionBlood urea nitrogen levelsLogistic regressionPrevious HF hospitalizationHeart failure hospitalizationReduced ejection fractionReceiver-operating characteristic curveRisk of deathBody mass indexBlood urea nitrogenUrea nitrogen levelsHealth status dataMean c-statisticKCCQ scoresTOPCAT trialFailure hospitalizationHeart failureHemoglobin levelsMass indexC-statisticHospitalizationUrea nitrogenMortality