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 riskFinancial burden, distress, and toxicity in cardiovascular disease
Slavin SD, Khera R, Zafar SY, Nasir K, Warraich HJ. Financial burden, distress, and toxicity in cardiovascular disease. American Heart Journal 2021, 238: 75-84. PMID: 33961830, DOI: 10.1016/j.ahj.2021.04.011.Peer-Reviewed Original ResearchConceptsCardiovascular diseaseFinancial burdenCommunity Health Worker IntegrationHigh-risk individualsComparative effectiveness studiesNon-medical needsHigh-cost interventionsHigh-cost treatmentsCVD managementEffectiveness studiesHealth systemPsychological distressInsurance coverageHealthcare policyBurdenDistressDiseaseSystem navigationInterventionCommunity-based initiativesPatientsPhysicians