Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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Emergency care providers share a compelling interest in developing an effective patient-centered, outcomes-based research agenda that can decrease variability in pediatric outcomes. The 2018 Academic Emergency Medicine Consensus Conference "Aligning the Pediatric Emergency Medicine Research Agenda to Reduce Health Outcome Gaps (AEMCC)" aimed to fulfill this role. ⋯ Topics that were explored and deliberated through subcommittee breakout sessions led by content experts included 1) pediatric emergency medical services research, 2) pediatric emergency medicine (PEM) research network collaboration, 3) PEM education for emergency medicine providers, 4) workforce development for PEM, and 5) enhancing collaboration across emergency departments (PEM practice in non-children's hospitals). The work product of this conference is a research agenda that aims to identify areas of future research, innovation, and scholarship in PEM.
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Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization. Earlier identification of need for hospital-level care could triage patients more efficiently to high- or low-resource ED tracks. Existing tools to predict disposition for pediatric asthma use only clinical data, perform best several hours into the ED stay, and are static or score-based. Machine learning offers a population-specific, dynamic option that allows real-time integration of available nonclinical data at triage. Our objective was to compare the performance of four common machine learning approaches, incorporating clinical data available at the time of triage with information about weather, neighborhood characteristics, and community viral load for early prediction of the need for hospital-level care in pediatric asthma. ⋯ Three of the four machine learning models performed well with decision trees preforming the worst. The gradient boosting machines model demonstrated a slight advantage over other approaches at predicting need for hospital-level care at the time of triage in pediatric patients presenting with asthma exacerbation. The addition of weight, SES, and weather data improved the performance of this model.