MC-MED, multimodal clinical monitoring in the emergency department
Abstract 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 response...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05419-5 |
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| _version_ | 1849334915709009920 |
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| author | Aman Kansal Emma Chen Boyang Tom Jin Pranav Rajpurkar David A. Kim |
| author_facet | Aman Kansal Emma Chen Boyang Tom Jin Pranav Rajpurkar David A. Kim |
| author_sort | Aman Kansal |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-26b1835be8534360ba3d07d2fec8730b |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-26b1835be8534360ba3d07d2fec8730b2025-08-20T03:45:27ZengNature PortfolioScientific Data2052-44632025-07-011211710.1038/s41597-025-05419-5MC-MED, multimodal clinical monitoring in the emergency departmentAman Kansal0Emma Chen1Boyang Tom Jin2Pranav Rajpurkar3David A. Kim4Department of Computer Science, Stanford UniversityDepartment of Biomedical Informatics, Harvard Medical SchoolDepartment of Emergency Medicine, Stanford UniversityDepartment of Biomedical Informatics, Harvard Medical SchoolDepartment of Emergency Medicine, Stanford UniversityAbstract 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.https://doi.org/10.1038/s41597-025-05419-5 |
| spellingShingle | Aman Kansal Emma Chen Boyang Tom Jin Pranav Rajpurkar David A. Kim MC-MED, multimodal clinical monitoring in the emergency department Scientific Data |
| title | MC-MED, multimodal clinical monitoring in the emergency department |
| title_full | MC-MED, multimodal clinical monitoring in the emergency department |
| title_fullStr | MC-MED, multimodal clinical monitoring in the emergency department |
| title_full_unstemmed | MC-MED, multimodal clinical monitoring in the emergency department |
| title_short | MC-MED, multimodal clinical monitoring in the emergency department |
| title_sort | mc med multimodal clinical monitoring in the emergency department |
| url | https://doi.org/10.1038/s41597-025-05419-5 |
| work_keys_str_mv | AT amankansal mcmedmultimodalclinicalmonitoringintheemergencydepartment AT emmachen mcmedmultimodalclinicalmonitoringintheemergencydepartment AT boyangtomjin mcmedmultimodalclinicalmonitoringintheemergencydepartment AT pranavrajpurkar mcmedmultimodalclinicalmonitoringintheemergencydepartment AT davidakim mcmedmultimodalclinicalmonitoringintheemergencydepartment |