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.

提出了应急护理多模态临床基准（MC-BEC），这是一个综合性基准，用于使用 2020-2022 年连续监测的超过 100K 个急诊科访问的数据集评估应急护理中的基础模型。 MC-BEC 关注于分钟到天的时间尺度上的临床相关预测任务，包括预测患者恶化、病情处理和急诊访问，并包括了一个带有训练 - 测试分割和评估指标的标准化评估框架。多模态数据集包括广泛的详细临床数据，包括分诊信息、先前诊断和药物、持续测量的生命体征、心电图和光电信号波形、整个访问期间的医嘱和药物管理、成像研究的自由文本报告、急诊诊断、处理和随后再访的信息。我们提供了每个预测任务的性能基准，以便评估多模态、多任务模型。我们相信 MC-BEC 将鼓励研究人员开发更有效、具有普适性和易获得性的多模态临床数据基础模型。