2024
Non-invasive Electrolyte Estimation Using Multi-lead ECG Data via Semi-Supervised Contrastive Learning with an Adaptive Loss
Nowroozilarki Z, Huang S, Khera R, Mortazavi B. Non-invasive Electrolyte Estimation Using Multi-lead ECG Data via Semi-Supervised Contrastive Learning with an Adaptive Loss. 2024, 00: 1-8. DOI: 10.1109/bhi62660.2024.10913552.Peer-Reviewed Original ResearchState-of-the-art modelsAdaptive lossSemi-supervised contrastive learningTrain machine learning-based modelsState-of-the-artClassification of electrocardiogramElectronic health record datasetLearning-based modelsMachine learning-based modelsContrastive learningLabel scarcityUnlabeled datasetRegression tasksClassification taskECG-dataRecord datasetData pointsLabeling frequencyDatasetTaskDataBackpropagationEncodingAccurate predictionLabelingData Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association
Armoundas A, Ahmad F, Bennett D, Chung M, Davis L, Dunn J, Narayan S, Slotwiner D, Wiley K, Khera R, Care P. Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation Genomic And Precision Medicine 2024, 17: e000095. PMID: 38779844, PMCID: PMC11703599, DOI: 10.1161/hcg.0000000000000095.Peer-Reviewed Original ResearchConceptsData interoperabilityDeployment of platformsInteroperability frameworkSoftware applicationsData integrationWearable devicesData ecosystemInteroperabilityMonitoring of cardiovascular diseasesQuality of dataDiverse health systemsClinical workflowTransform health careDataScientific statementCardiovascular diseaseClinical contentAmerican Heart AssociationCaregivers' accessHealth systemHealth care
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
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