2022
A Clinical Framework for Evaluating Machine Learning Studies ∗
Ghazi L, Ahmad T, Wilson FP. A Clinical Framework for Evaluating Machine Learning Studies ∗. JACC Heart Failure 2022, 10: 648-650. PMID: 35963817, DOI: 10.1016/j.jchf.2022.07.002.Commentaries, Editorials and Letters
2021
A neutrophil activation signature predicts critical illness and mortality in COVID-19
Meizlish ML, Pine AB, Bishai JD, Goshua G, Nadelmann ER, Simonov M, Chang CH, Zhang H, Shallow M, Bahel P, Owusu K, Yamamoto Y, Arora T, Atri DS, Patel A, Gbyli R, Kwan J, Won CH, Dela Cruz C, Price C, Koff J, King BA, Rinder HM, Wilson FP, Hwa J, Halene S, Damsky W, van Dijk D, Lee AI, Chun HJ. A neutrophil activation signature predicts critical illness and mortality in COVID-19. Blood Advances 2021, 5: 1164-1177. PMID: 33635335, PMCID: PMC7908851, DOI: 10.1182/bloodadvances.2020003568.Peer-Reviewed Original ResearchConceptsCritical illnessHealth system databaseNeutrophil activationCOVID-19Neutrophil activation signatureSevere COVID-19Intensive care unitGranulocyte colony-stimulating factorHigh mortality rateColony-stimulating factorSystem databaseHepatocyte growth factorClinical decompensationNeutrophil countImmune hyperactivationCare unitEarly elevationLipocalin-2Interleukin-8Longitudinal cohortClinical dataMortality ratePatientsIllnessActivation signature
2020
A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children
Sandokji I, Yamamoto Y, Biswas A, Arora T, Ugwuowo U, Simonov M, Saran I, Martin M, Testani JM, Mansour S, Moledina DG, Greenberg JH, Wilson FP. A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children. Journal Of The American Society Of Nephrology 2020, 31: 1348-1357. PMID: 32381598, PMCID: PMC7269342, DOI: 10.1681/asn.2019070745.Peer-Reviewed Original ResearchConceptsExternal validation cohortValidation cohortElectronic health recordsSevere AKIClinical risk stratification toolDevelopment of AKIHealth recordsRisk stratification toolInternal validation cohortLength of stayCharacteristic curveElectronic medical recordsNeonatal AKIInpatient mortalitySecondary outcomesHospital admissionPrimary outcomeHospitalized childrenCreatinine valuesMedical recordsStudy populationAKICohortChildrenPredictive variables
2019
Machine Learning to Predict Acute Kidney Injury
Wilson FP. Machine Learning to Predict Acute Kidney Injury. American Journal Of Kidney Diseases 2019, 75: 965-967. PMID: 31677894, PMCID: PMC7735021, DOI: 10.1053/j.ajkd.2019.08.010.Commentaries, Editorials and Letters
2018
Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study
Huang C, Murugiah K, Mahajan S, Li SX, Dhruva SS, Haimovich JS, Wang Y, Schulz WL, Testani JM, Wilson FP, Mena CI, Masoudi FA, Rumsfeld JS, Spertus JA, Mortazavi BJ, Krumholz HM. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. PLOS Medicine 2018, 15: e1002703. PMID: 30481186, PMCID: PMC6258473, DOI: 10.1371/journal.pmed.1002703.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAgedClinical Decision-MakingData MiningDecision Support TechniquesFemaleHumansMachine LearningMaleMiddle AgedPercutaneous Coronary InterventionProtective FactorsRegistriesReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeConceptsPercutaneous coronary interventionNational Cardiovascular Data RegistryRisk prediction modelAKI eventsAKI riskCoronary interventionAKI modelMean ageCardiology-National Cardiovascular Data RegistryAcute kidney injury riskAKI risk predictionRetrospective cohort studyIdentification of patientsCandidate variablesAvailable candidate variablesCohort studyPCI proceduresPoint of careBrier scoreAmerican CollegeData registryPatientsCalibration slopeInjury riskSame cohort