2023
Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging
Joel M, Avesta A, Yang D, Zhou J, Omuro A, Herbst R, Krumholz H, Aneja S. Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging. Cancers 2023, 15: 1548. PMID: 36900339, PMCID: PMC10000732, DOI: 10.3390/cancers15051548.Peer-Reviewed Original ResearchAdversarial imagesDeep learning modelsDL modelsDetection modelLearning modelConvolutional neural networkDetection schemeAdversarial detectionDefense techniquesMachine learningMedical imagesAdversarial perturbationsInput imageAdversarial trainingNeural networkArt performanceMagnetic resonance imagingGradient descentPixel valuesHigh accuracyImagesBrain magnetic resonance imagingAbsence of malignancyClassificationScheme
2022
Neural Natural Language Processing for unstructured data in electronic health records: A review
Li I, Pan J, Goldwasser J, Verma N, Wong W, Nuzumlalı M, Rosand B, Li Y, Zhang M, Chang D, Taylor R, Krumholz H, Radev D. Neural Natural Language Processing for unstructured data in electronic health records: A review. Computer Science Review 2022, 46: 100511. DOI: 10.1016/j.cosrev.2022.100511.Peer-Reviewed Original ResearchNatural language processingElectronic health recordsLanguage processingDeep learning approachHealth recordsRule-based systemNew neural networkVariety of tasksUnstructured dataUnstructured textKnowledge graphEHR applicationsDigital collectionsNeural networkNLP methodsLearning approachWord embeddingsSurvey paperSecondary useMedical dialogueHealthcare eventsTaskProcessingMultilingualityInterpretabilityAutomated 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
Use 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 ResearchConceptsMachine 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