2019
Emergency care surveillance and emergency care registries in low-income and middle-income countries: conceptual challenges and future directions for research
Mowafi H, Ngaruiya C, O'Reilly G, Kobusingye O, Kapil V, Rubiano A, Ong M, Puyana JC, Rahman AF, Jooma R, Beecroft B, Razzak J. Emergency care surveillance and emergency care registries in low-income and middle-income countries: conceptual challenges and future directions for research. BMJ Global Health 2019, 4: e001442. PMID: 31406601, PMCID: PMC6666805, DOI: 10.1136/bmjgh-2019-001442.Peer-Reviewed Original ResearchCare surveillanceEmergency careMiddle-income countriesRegistry researchStandard clinical formsDisability-adjusted life yearsPopulation-based researchUS National InstitutesClinical formsGlobal emergency careOverall burdenRegistry dataEmergency unitLongitudinal surveillanceLife yearsFogarty International CenterGlobal deathsLMIC settingsWorking GroupHealth recordsCareNational InstituteSurveillancePaucity of researchBurden
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