MC-MED: Multimodal Clinical Monitoring Dataset
Catalog Summary
"MC-MED provides high-resolution multimodal clinical and physiological data from 118,385 adult emergency department visits, supporting real-time monitoring and AI-based medical research."
Catalog Notes
Dataset Overview
MC-MED represents a landmark in healthcare IoT research, providing high-resolution physiological data from over 118,000 patient visits. This dataset is uniquely positioned to support the development of real-time monitoring algorithms.
Data Modalities and Sensors
- Vital Signs: Continuous monitoring of heart rate, respiratory rate, and oxygen saturation (SpO2).
- Waveform Data: High-fidelity ECG, PPG, and respiratory waveforms captured via bedside IoT monitors.
- Clinical Context: Comprehensive patient demographics and laboratory results.
Research Potential
Researchers can utilize this data for early warning systems and AI-based arrhythmia detection using continuous physiological streams.
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on University.
Preview on UniversityCite This Dataset
The dataset creators ask users of this dataset to cite the accompanying paper. Use one of the verified formats below.
Kansal, A., Chen, E., Jin, T., Rajpurkar, P., & Kim, D. (2025). Multimodal Clinical Monitoring in the Emergency Department (MC-MED). PhysioNet. https://doi.org/10.13026/jz99-4j81
Source metadata: University (2025) · DOI: 10.13026/jz99-4j81
Indexed by IoTDataset.com on Feb 13, 2026
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