2023
An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model
Charkoftaki G, Aalizadeh R, Santos-Neto A, Tan W, Davidson E, Nikolopoulou V, Wang Y, Thompson B, Furnary T, Chen Y, Wunder E, Coppi A, Schulz W, Iwasaki A, Pierce R, Cruz C, Desir G, Kaminski N, Farhadian S, Veselkov K, Datta R, Campbell M, Thomaidis N, Ko A, Thompson D, Vasiliou V. An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model. Human Genomics 2023, 17: 80. PMID: 37641126, PMCID: PMC10463861, DOI: 10.1186/s40246-023-00521-4.Peer-Reviewed Original ResearchConceptsCOVID-19 patientsDisease severityViral outbreaksFuture viral outbreaksLength of hospitalizationIntensive care unitWorse disease prognosisLife-threatening illnessEffective medical interventionsCOVID-19Clinical decision treeGlucuronic acid metabolitesNew potential biomarkersHospitalization lengthCare unitComorbidity dataSerotonin levelsDisease progressionHealthy controlsPatient outcomesDisease prognosisPatient transferPatientsHealthcare resourcesPotential biomarkers
2021
Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review
Herman DS, Rhoads DD, Schulz WL, Durant TJS. Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review. Clinical Chemistry 2021, 67: 1466-1482. PMID: 34557917, DOI: 10.1093/clinchem/hvab165.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMeSH KeywordsArtificial IntelligenceDelivery of Health CareHumansLaboratoriesMachine LearningMedicineConceptsArtificial intelligenceML systemsModern artificial intelligenceAI/MLAdoption of MLExpert human operatorMachine learning methodsModern computingCommunication infrastructureHuman operatorLearning methodsInformation systemsWidespread digitizationIntelligenceHealth informationTechnologyImplementationComputingBrief introductionBest practicesSystemDigitizationInfrastructureTaskInformation
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