2024
Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels
Spies N, Militello L, Farnsworth C, El-Khoury J, Durant T, Zaydman M. Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels. Clinical Chemistry 2024, hvae168. PMID: 39545815, DOI: 10.1093/clinchem/hvae168.Peer-Reviewed Original ResearchSHapley Additive exPlanationsLearning approachDetection of contamination eventsUnsupervised learning approachLearning-based methodsMachine learning-based methodsEnsemble learning approachMachine learning pipelineEnsemble learningLearning pipelineMatthews correlation coefficientAlgorithmic fairnessReal worldSHapley Additive exPlanations valuesCurrent workflowsClinical workflowWorkflowOperational burdenBasic metabolic panelIntravenous (IVPipelineInternal validation setValidation setFlagging ratesPerformance assessment
2021
Supervised machine learning in the mass spectrometry laboratory: A tutorial
Lee ES, Durant TJS. Supervised machine learning in the mass spectrometry laboratory: A tutorial. Journal Of Mass Spectrometry And Advances In The Clinical Lab 2021, 23: 1-6. PMID: 34984411, PMCID: PMC8692990, DOI: 10.1016/j.jmsacl.2021.12.001.Peer-Reviewed Educational MaterialsCapabilities of MLMachine learning methodsHigh-dimensional datasetsClassification problemSupervised machineSupervised MLComputer scienceDiscrete data elementsLearning methodsResult qualityData elementsML practicesDatasetSoftwareScientist communityInherent relationshipTutorialMass spectrometry dataPromising synergyMS datasetsData analysisMass spectrometry laboratoriesDistant natureRecent yearsWorkflow