Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A
  Comprehensive Benchmark for Evaluating Foundation Models in Emergency
  Medicine
Emma Chen Aman Kansal Julie Chen Boyang Tom Jin Julia Rachel Reisler David A Kim Pranav Rajpurkar
We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a
comprehensive benchmark for evaluating foundation models in Emergency Medicine
using a dataset of 100K+ continuously monitored Emergency Department visits
from 2020-2022. MC-BEC focuses on clinically relevant prediction tasks at
timescales from minutes to days, including predicting patient decompensation,
disposition, and emergency department (ED) revisit, and includes a standardized
evaluation framework with train-test splits and evaluation metrics. The
multimodal dataset includes a wide range of detailed clinical data, including
triage information, prior diagnoses and medications, continuously measured
vital signs, electrocardiogram and photoplethysmograph waveforms, orders placed
and medications administered throughout the visit, free-text reports of imaging
studies, and information on ED diagnosis, disposition, and subsequent revisits.
We provide performance baselines for each prediction task to enable the
evaluation of multimodal, multitask models. We believe that MC-BEC will
encourage researchers to develop more effective, generalizable, and accessible
foundation models for multimodal clinical data.