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The demand for emergency department (ED) services is increasing across theglobe, particularly during the current COVID-19 pandemic. Clinical triage andrisk assessment have become increasingly challenging due to the shortage ofmedical resources and the strain on hospital infrastructure caused by thepandemic. As a result of the widespread use of electronic health records(EHRs), we now have access to a vast amount of clinical data, which allows usto develop predictive models and decision support systems to address thesechallenges. To date, however, there are no widely accepted benchmark ED triageprediction models based on large-scale public EHR data. An open-sourcebenchmarking platform would streamline research workflows by eliminatingcumbersome data preprocessing, and facilitate comparisons among differentstudies and methodologies. In this paper, based on the Medical Information Martfor Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developeda publicly available benchmark suite for ED triage predictive models andcreated a benchmark dataset that contains over 400,000 ED visits from 2011 to2019. We introduced three ED-based outcomes (hospitalization, criticaloutcomes, and 72-hour ED reattendance) and implemented a variety of popularmethodologies, ranging from machine learning methods to clinical scoringsystems. We evaluated and compared the performance of these methods againstbenchmark tasks. Our codes are open-source, allowing anyone with MIMIC-IV-EDdata access to perform the same steps in data processing, benchmark modelbuilding, and experiments. This study provides future researchers withinsights, suggestions, and protocols for managing raw data and developing risktriaging tools for emergency care.

Benchmarking emergency department triage prediction models with machine learning and large public electronic health records