2024
Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.
Shimbo D, Shah R, Abdalla M, Agarwal R, Ahmad F, Anaya G, Attia Z, Bull S, Chang A, Commodore-Mensah Y, Ferdinand K, Kawamoto K, Khera R, Leopold J, Luo J, Makhni S, Mortazavi B, Oh Y, Savage L, Spatz E, Stergiou G, Turakhia M, Whelton P, Yancy C, Iturriaga E. Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report. Hypertension 2024 PMID: 39011653, DOI: 10.1161/hypertensionaha.124.22095.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMachine learning toolsManagement of hypertensionNational HeartArtificial intelligenceBlood InstitutePredictive of incident hypertensionHealth care systemImplementation challengesDiverse group of stakeholdersAI toolsPopulation healthMeasurement of blood pressureCare systemHealth careIncident hypertensionHypertension riskEra of artificial intelligenceHypertension diagnosisLearning toolsManaging hypertensionHypertension-related complicationsAntihypertensive medicationsHealthPublic healthGroups of stakeholdersEfficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning
Holste G, Oikonomou E, Mortazavi B, Wang Z, Khera R. Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning. Communications Medicine 2024, 4: 133. PMID: 38971887, PMCID: PMC11227494, DOI: 10.1038/s43856-024-00538-3.Peer-Reviewed Original ResearchSelf-supervised learningTransfer learningTraining dataEchocardiogram videosPortion of labelled dataStandard transfer learning approachContrastive self-supervised learningSelf-supervised learning approachLearning approachImage recognition tasksState-of-the-artContrastive learning approachFine-tuningTransfer learning approachMedical image diagnosisCardiac disease diagnosisContrastive learningVideo framesLabeled datasetLabeled dataExpert labelsClassification performanceMedical imagesRecognition taskVideoMacronutrient Constraints and Priors Improve Carbohydrate Prediction From Continuous Glucose Monitors
Das A, Do E, Glantz N, Bevier W, Santiago R, Kerr D, Mortazavi B, Gutierrez-Osuna R. Macronutrient Constraints and Priors Improve Carbohydrate Prediction From Continuous Glucose Monitors. Current Developments In Nutrition 2024, 8: 102290. DOI: 10.1016/j.cdnut.2024.102290.Peer-Reviewed Original ResearchSOFA score performs worse than age for predicting mortality in patients with COVID-19
Sherak R, Sajjadi H, Khimani N, Tolchin B, Jubanyik K, Taylor R, Schulz W, Mortazavi B, Haimovich A. SOFA score performs worse than age for predicting mortality in patients with COVID-19. PLOS ONE 2024, 19: e0301013. PMID: 38758942, PMCID: PMC11101117, DOI: 10.1371/journal.pone.0301013.Peer-Reviewed Original ResearchConceptsCrisis standards of careIn-hospital mortalityIntensive care unitAcademic health systemSequential Organ Failure Assessment scoreCohort of intensive care unitSequential Organ Failure AssessmentStandard of careLogistic regression modelsMortality predictionPredicting in-hospital mortalityHealth systemUnivariate logistic regression modelCrisis standardsDisease morbidityCOVID-19Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications
Huang S, Jafari R, Mortazavi B. Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications. IEEE Open Journal Of Engineering In Medicine And Biology 2024, 5: 330-338. PMID: 38899025, PMCID: PMC11186651, DOI: 10.1109/ojemb.2024.3398444.Peer-Reviewed Original ResearchData preprocessing frameworkPreprocessing frameworkMachine learningInternet of Medical ThingsMean-absolute-errorHealth monitoring tasksEnd-to-endRoot-mean-square-errorMedical ThingsSensor dataAdaptation frameworkMonitoring tasksSystolic blood pressure estimationMedical tasksWearable recordingsDatasetBlood pressure estimationPulsatile signalsTaskDataMedical applicationsFrameworkInternetWearableThingsPredicting Major Adverse Events in Patients Undergoing Transcatheter Left Atrial Appendage Occlusion
Faridi K, Ong E, Zimmerman S, Varosy P, Friedman D, Hsu J, Kusumoto F, Mortazavi B, Minges K, Pereira L, Lakkireddy D, Koutras C, Denton B, Mobayed J, Curtis J, Freeman J. Predicting Major Adverse Events in Patients Undergoing Transcatheter Left Atrial Appendage Occlusion. Circulation Arrhythmia And Electrophysiology 2024, 17: e012424. PMID: 38390713, PMCID: PMC11021146, DOI: 10.1161/circep.123.012424.Peer-Reviewed Original ResearchNational Cardiovascular Data RegistryLeft atrial appendage occlusionIn-hospital major adverse eventsMajor adverse eventsBedside risk scoreRisk scoreData RegistryIncreased fall riskAdverse eventsQuality improvement effortsWatchman FLXAppendage occlusionFall riskLeft atrial appendage occlusion procedureRegistry dataImprovement effortsRisk of in-hospital major adverse eventsPredicting major adverse eventsLogistic regressionAdverse event ratesModerate discriminationClinically relevant variablesFemale sexAtrial fibrillation terminationLAAO proceduresBiometric contrastive learning for data-efficient deep learning from electrocardiographic images
Sangha V, Khunte A, Holste G, Mortazavi B, Wang Z, Oikonomou E, Khera R. Biometric contrastive learning for data-efficient deep learning from electrocardiographic images. Journal Of The American Medical Informatics Association 2024, 31: 855-865. PMID: 38269618, PMCID: PMC10990541, DOI: 10.1093/jamia/ocae002.Peer-Reviewed Original ResearchLabeled training dataContrastive learningECG imagesLabeled dataTraining dataDeep learningProportions of labeled dataArtificial intelligenceSelf-supervised contrastive learningTraditional supervised learningConvolutional neural networkHeld-out test setSupervised learningPretraining strategyBiometric signatureImageNet initializationPretraining approachNeural networkImageNetAI modelsImage objectsTest setLearningDetect atrial fibrillationEquivalent performance
2023
Modeling user choice behavior under data corruption: Robust learning of the latent decision threshold model
Lin F, Qian X, Mortazavi B, Wang Z, Huang S, Chen C. Modeling user choice behavior under data corruption: Robust learning of the latent decision threshold model. IISE Transactions 2023, 56: 1307-1320. DOI: 10.1080/24725854.2023.2279080.Peer-Reviewed Original ResearchData corruptionReal-world user dataUser-centered systemsRobust learning frameworkRobust learning methodNew mobile appUser dataUser behaviorLearning frameworkLearning methodsArt methodsMobile appsRobust learningUsers' choice behaviorPrediction accuracyBad actorsUsersNew applicationsConsiderable research effortFrameworkResearch effortsModel estimationRecent yearsAlgorithmAppsJoint Embedding of Food Photographs and Blood Glucose for Improved Calorie Estimation
Zhang L, Huang S, Das A, Do E, Glantz N, Bevier W, Santiago R, Kerr D, Gutierrez-Osuna R, Mortazavi B. Joint Embedding of Food Photographs and Blood Glucose for Improved Calorie Estimation. 2023, 00: 1-4. DOI: 10.1109/bhi58575.2023.10313421.Peer-Reviewed Original ResearchImage dataNormalized root mean squared errorCalorie estimationLate fusion approachAttention-based transformersFood image dataVision TransformerImage informationFusion approachJoint embeddingRoot mean squared errorAverage normalized root mean squared errorMean squared errorInterstitial glucose dataPeople's health conditionsDiet monitoringHealth conditionsSquared errorType 2 diabetesCGM dataInformationBlood glucoseMeal intakeFood photographsAccurate estimationArterialNet: Arterial Blood Pressure Reconstruction
Huang S, Jafari R, Mortazavi B. ArterialNet: Arterial Blood Pressure Reconstruction. 2023, 00: 1-4. DOI: 10.1109/bhi58575.2023.10313518.Peer-Reviewed Original ResearchEarlier identification of hypertensive events in a telemonitoring system
Do E, Lavu S, Kum H, Mortazavi B. Earlier identification of hypertensive events in a telemonitoring system. 2023, 00: 1-4. DOI: 10.1109/bsn58485.2023.10330992.Peer-Reviewed Original ResearchModeling the effect of non-exercise activity on peak post-prandial glucose in diabetes
Do E, Das A, Glanz N, Bevier W, Santiago R, Kerr D, Gutierrez-Osuna R, Mortazavi B. Modeling the effect of non-exercise activity on peak post-prandial glucose in diabetes. 2023, 00: 1-4. DOI: 10.1109/bsn58485.2023.10331072.Peer-Reviewed Original ResearchNon-exercise activity thermogenesisPost-prandial glucosePeak glucoseNon-exercise activityGlucose surgesPostprandial glucoseBlood glucoseGlucose excursionsPostprandial exerciseActivity thermogenesisClinical relevanceFree-living environmentGlucose reductionModerate intensityActivity intensityDiabetesEffects modelGlucoseExerciseGreater attenuationMinutesMealDurationPredicting Real-time, Recurrent Adverse Invasive Ventilation from Clinical Data Streams
Pakbin A, Nowroozilarki Z, Lee D, Mortazavi B. Predicting Real-time, Recurrent Adverse Invasive Ventilation from Clinical Data Streams. 2023, 00: 1-4. DOI: 10.1109/bsn58485.2023.10331225.Peer-Reviewed Original ResearchMachine learning methodsClinical data streamsReal-time risk monitoringNon-recurring eventsData streamsLearning methodsData changesEHR dataIntensive care unit patientsElectronic health record dataCare unit patientsReal-time monitoringAnalysis toolsHealth record dataQuality of careEvent dataICU stayInvasive ventilationTime-dependent covariatesUnit patientsWarning systemTremendous opportunitiesRisk monitoringSurvival analysisPersonalized treatmentClinical 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 Research
2014
Near-Realistic Mobile Exergames With Wireless Wearable Sensors
Mortazavi B, Nyamathi S, Lee SI, Wilkerson T, Ghasemzadeh H, Sarrafzadeh M. Near-Realistic Mobile Exergames With Wireless Wearable Sensors. IEEE Journal Of Biomedical And Health Informatics 2014, 18: 449-456. PMID: 24608050, DOI: 10.1109/jbhi.2013.2293674.Peer-Reviewed Original Research
2012
A Monte Carlo Approach to Biomedical Time Series Search
Woodbridge J, Mortazavi B, Sarrafzadeh M, Bui AA. A Monte Carlo Approach to Biomedical Time Series Search. 2023 IEEE International Conference On Bioinformatics And Biomedicine (BIBM) 2012, 1: 1-6. PMID: 27617164, PMCID: PMC5016193, DOI: 10.1109/bibm.2012.6392646.Peer-Reviewed Original ResearchQuery resultsInformation gainReal-world biomedical datasetsTime-series subsequence matchingTime series searchCase-based diagnosisDiscovery of trendsHigh-dimensional dataHealth care informaticsSubsequence matchingBiomedical datasetsDimensional dataMatching schemeVariety of areasTraditional researchMatchingExcellent performanceSearch criteriaOverheadGaussian distributionParallelizationInformaticsDatasetSearchingMonte Carlo sampling method
Others
- Mortazavi, Bobak; Alsharufa, Nabil; Lee, Sunghoon Ivan; Lan, Mars; Sarrafzadeh, Majid; Chronley, Michael; Roberts, Christian K., "MET calculations from on-body accelerometers for exergaming movements," Body Sensor Networks (BSN), 2013 IEEE International Conference on , vol., no., pp.1,6, 6-9 May 2013Peer-Reviewed Original Research
- Mortazavi, B.; Kin Chung Chu; Xialong Li; Tai, J.; Kotekar, S.; Sarrafzadeh, M., "Near-Realistic Motion Video Games with Enforced Activity," Wearable and Implantable Body Sensor Networks (BSN), 2012 Ninth International Conference on , vol., no., pp.28,33, 9-12 May 2012.Peer-Reviewed Original Research
- Sunghoon Ivan Lee, Hassan Ghasemzadeh, Bobak Mortazavi, Mars Lan, Nabil Alshurafa, Michael Ong, and Majid Sarrafzadeh. 2013. Remote patient monitoring: what impact can data analytics have on cost?. In Proceedings of the 4th Conference on Wireless Health (WH '13). ACM, New York, NY, USA, , Article 4 , 8 pages.Peer-Reviewed Original Research