Kansal Aman, Chen Emma, Jin Boyang Tom, Rajpurkar Pranav, Kim David A
Department of Computer Science, Stanford University, Stanford, USA.
Department of Biomedical Informatics, Harvard Medical School, Boston, USA.
Sci Data. 2025 Jul 1;12(1):1094. doi: 10.1038/s41597-025-05419-5.
Emergency Department (ED) patients often present with undiagnosed complaints, and can exhibit rapidly evolving physiology. Therefore, data from continuous physiologic monitoring, in addition to the electronic health record, is essential to understand the acute course of illness and responses to interventions. The complexity of ED care and the large amount of unstructured multimodal data it produces have limited the accessibility of detailed ED data for research. We release Multimodal Clinical Monitoring in the Emergency Department (MC-MED), a comprehensive, multimodal, and de-identified clinical and physiological dataset. MC-MED includes 118,385 adult ED visits to an academic medical center from 2020 to 2022. Data include continuously monitored vital signs, physiologic waveforms (electrocardiogram, photoplethysmogram, respiration), patient demographics, medical histories, orders, medication administrations, laboratory and imaging results, and visit outcomes. MC-MED is the first dataset to combine detailed physiologic monitoring with clinical events and outcomes for a large, diverse ED population.
急诊科(ED)患者常常带着未确诊的病症前来就诊,且生理状况可能迅速变化。因此,除电子健康记录外,连续生理监测数据对于了解疾病的急性病程及对干预措施的反应至关重要。急诊护理的复杂性及其产生的大量非结构化多模态数据限制了用于研究的详细急诊数据的可获取性。我们发布了急诊科多模态临床监测数据集(MC-MED),这是一个全面、多模态且经过去标识化处理的临床和生理数据集。MC-MED包含了2020年至2022年期间在一家学术医疗中心进行的118,385例成人急诊就诊记录。数据包括连续监测的生命体征、生理波形(心电图、光电容积脉搏波、呼吸)、患者人口统计学信息、病史、医嘱、用药情况、实验室和影像学检查结果以及就诊结局。MC-MED是首个将详细的生理监测与大量不同急诊患者的临床事件及结局相结合的数据集。