2021
Machine Learning in Emergency Medicine: Keys to Future Success
Taylor RA, Haimovich AD. Machine Learning in Emergency Medicine: Keys to Future Success. Academic Emergency Medicine 2021, 28: 263-267. PMID: 33277733, DOI: 10.1111/acem.14189.Peer-Reviewed Original Research
2018
Predicting urinary tract infections in the emergency department with machine learning
Taylor RA, Moore CL, Cheung KH, Brandt C. Predicting urinary tract infections in the emergency department with machine learning. PLOS ONE 2018, 13: e0194085. PMID: 29513742, PMCID: PMC5841824, DOI: 10.1371/journal.pone.0194085.Peer-Reviewed Original ResearchConceptsExtreme gradient boostingGradient boostingXGBoost modelLarge diverse setHigh diagnostic error rateMachineAlgorithmXGBoostError rateDiverse setInadequate diagnostic performancePredictive modelSetPrediction toolsDiagnostic error rateBoostingCommon emergency department (ED) diagnosisFull setModel
2016
Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach
Taylor RA, Pare JR, Venkatesh AK, Mowafi H, Melnick ER, Fleischman W, Hall MK. Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach. Academic Emergency Medicine 2016, 23: 269-278. PMID: 26679719, PMCID: PMC5884101, DOI: 10.1111/acem.12876.Peer-Reviewed Original ResearchConceptsMachine learning approachesElectronic health recordsLearning approachPredictive analyticsMachine learning techniquesRandom forest modelClinical decision support systemBig Data DrivenDecision support systemForest modelLearning techniquesUse casesData-DrivenFacilitate automationTraditional analytic techniquesAnalyticsSupport systemSimple heuristicsNew analyticsHealth recordsSmall setTree modelQuestion of generalizabilityPrediction modelDecision rules