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
2014
Readmission Rates and Long-Term Hospital Costs Among Survivors of an In-Hospital Cardiac Arrest
Chan PS, Nallamothu BK, Krumholz HM, Curtis LH, Li Y, Hammill BG, Spertus JA. Readmission Rates and Long-Term Hospital Costs Among Survivors of an In-Hospital Cardiac Arrest. Circulation Cardiovascular Quality And Outcomes 2014, 7: 889-895. PMID: 25351479, PMCID: PMC4241155, DOI: 10.1161/circoutcomes.114.000925.Peer-Reviewed Original ResearchConceptsHospital cardiac arrestCardiac arrestInpatient costsMean inpatient costsLarge national registryInpatient resource useNeurological statusReadmission patternsHospital dispositionPatient demographicsReadmission ratesMean ageInpatient useNational registryYounger ageReadmissionArrestAgeYearsDaysPatientsRegistryResource useSurvivors