  The demand for emergency department (ED) services is increasing across the
globe, particularly during the current COVID-19 pandemic. Clinical triage and
risk assessment have become increasingly challenging due to the shortage of
medical resources and the strain on hospital infrastructure caused by the
pandemic. 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 us
to develop predictive models and decision support systems to address these
challenges. To date, however, there are no widely accepted benchmark ED triage
prediction models based on large-scale public EHR data. An open-source
benchmarking platform would streamline research workflows by eliminating
cumbersome data preprocessing, and facilitate comparisons among different
studies and methodologies. In this paper, based on the Medical Information Mart
for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed
a publicly available benchmark suite for ED triage predictive models and
created a benchmark dataset that contains over 400,000 ED visits from 2011 to
2019. We introduced three ED-based outcomes (hospitalization, critical
outcomes, and 72-hour ED reattendance) and implemented a variety of popular
methodologies, ranging from machine learning methods to clinical scoring
systems. We evaluated and compared the performance of these methods against
benchmark tasks. Our codes are open-source, allowing anyone with MIMIC-IV-ED
data access to perform the same steps in data processing, benchmark model
building, and experiments. This study provides future researchers with
insights, suggestions, and protocols for managing raw data and developing risk
triaging tools for emergency care.

Machine Learning