2023
Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices
Khunte A, Sangha V, Oikonomou E, Dhingra L, Aminorroaya A, Mortazavi B, Coppi A, Brandt C, Krumholz H, Khera R. Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices. Npj Digital Medicine 2023, 6: 124. PMID: 37433874, PMCID: PMC10336107, DOI: 10.1038/s41746-023-00869-w.Peer-Reviewed Original ResearchArtificial intelligenceRandom Gaussian noiseNoisy electrocardiogramGaussian noiseElectrocardiogram (ECGWearable devicesSingle-lead electrocardiogramPortable devicesSNRWearableNoiseDevice noiseRepositoryAI-based screeningIntelligenceDetectionDevicesNoise sourcesVentricular systolic dysfunctionModelElectrocardiogramSingle-lead electrocardiographyTrainingComparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation
Avesta A, Hossain S, Lin M, Aboian M, Krumholz H, Aneja S. Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation. Bioengineering 2023, 10: 181. PMID: 36829675, PMCID: PMC9952534, DOI: 10.3390/bioengineering10020181.Peer-Reviewed Original ResearchLimited training dataDice scoreComputational memoryTraining dataBrain imagesDeep-learning methodsHigher Dice scoresSegmentation accuracyAuto-segmentation modelComputational speedPerformance metricsOne-sliceAuto-SegmentationBetter performanceConsecutive slicesImagesDeploymentLowest Dice scoresMemoryPerformanceTrainingMetricsModelAccuracyData
2022
Automated multilabel diagnosis on electrocardiographic images and signals
Sangha V, Mortazavi BJ, Haimovich AD, Ribeiro AH, Brandt CA, Jacoby DL, Schulz WL, Krumholz HM, Ribeiro ALP, Khera R. Automated multilabel diagnosis on electrocardiographic images and signals. Nature Communications 2022, 13: 1583. PMID: 35332137, PMCID: PMC8948243, DOI: 10.1038/s41467-022-29153-3.Peer-Reviewed Original ResearchConceptsConvolutional neural networkArtificial intelligenceApplication of AISignal-based dataSignal-based modelElectrocardiographic imagesECG imagesGrad-CAMImage-based modelsNeural networkDiagnosis modelECG signalsImagesClinical labelsValidation setLabelsExternal validation setMultilabelIntelligenceNetworkApplicationsModelBroad useSetBroader setting
2021
Multicentre methodological study to create a publicly available score of hospital financial standing in the USA
Zinoviev R, Krumholz HM, Ciccarone R, Antle R, Forman HP. Multicentre methodological study to create a publicly available score of hospital financial standing in the USA. BMJ Open 2021, 11: e046500. PMID: 34301654, PMCID: PMC8311305, DOI: 10.1136/bmjopen-2020-046500.Peer-Reviewed Original ResearchConceptsCredit ratingsFinancial dataBond ratingsMoody’s credit ratingsHospital's financial standingHospital financial dataFinancial literatureFinancial stabilityFinancial indicatorsFinancial scoresFinancial trendsFinancial healthFinancial standingFinancial metricsGreater shareMedicare dischargesWeighted variablesShareHospital operationsUnique variablesSingle composite scoreVariablesMoodyHealth systemModelAssociation of Graduate Medical Education With Hospital Performance and Patient Outcomes
Zinoviev R, Krumholz HM, Pirruccio K, Forman H. Association of Graduate Medical Education With Hospital Performance and Patient Outcomes. JAMA Network Open 2021, 4: e2034196. PMID: 33507257, PMCID: PMC7844596, DOI: 10.1001/jamanetworkopen.2020.34196.Peer-Reviewed Original ResearchConceptsResident academic performanceFinancial standingGME fundingFinancial scoresFinancial performance scoreHospital's financial standingFunding dataHospital financial dataHospital performanceFinancial dataEconomic evaluationAnnual subsidyEducation fundingHospital operationsMedical education fundingHospital Compare databaseSubsidiesRegression modelsFundingLinear regression modelsCostRecipient hospitalModelBoard certification examinationStanding
2020
The Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers.
Mori M, Khera R, Lin Z, Ross JS, Schulz W, Krumholz HM. The Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers. Methodist DeBakey Cardiovascular Journal 2020, 16: 212-219. PMID: 33133357, PMCID: PMC7587314, DOI: 10.14797/mdcj-16-3-212.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsLearning health systemLearning systemCommon data modelDynamic learning systemAdvanced analyticsBig dataData assetsData modelDigital solutionsCustomer interactionContinuous learningKnowledge generationEffective useConceptual modelAnalyticsSystemGoogleHealth systemLearningComparable scaleModelDataCompanies
2005
Standards for Statistical Models Used for Public Reporting of Health Outcomes
Krumholz HM, Brindis RG, Brush JE, Cohen DJ, Epstein AJ, Furie K, Howard G, Peterson ED, Rathore SS, Smith SC, Spertus JA, Wang Y, Normand SL. Standards for Statistical Models Used for Public Reporting of Health Outcomes. Circulation 2005, 113: 456-462. PMID: 16365198, DOI: 10.1161/circulationaha.105.170769.Peer-Reviewed Original Research