Papers › VitalDB
VitalDB
Hyung-Chul Lee, Yoonsang Park, Soo Bin Yoon, Seong Mi Yang, Dongnyeok Park, Chul-Woo Jung
In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series bio-signals and their correlations is a difficult task even for experienced anaesthesiologists. Although recent advanced machine learning technologies have shown promising results in bio-signal analysis, research and development in this area is relatively slow due to the lack of bio-signal datasets for learning. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. This dataset includes high-resolution multi-parameter intraoperative vital signs from 6,388 non-cardiac surgery in the 10 operating rooms at a tertiary academic hospital. A total of 557,622 waveform and numeric data from 197 monitoring parameters were recorded using Vital Recorder and 77 clinical information were extracted from electronic medical records. All data is stored in the public cloud after anonymization. The dataset can be freely accessed and analysed using application programming interfaces and Python library. The VitalDB public dataset is expected to be a valuable data resource for bio-signal research and development.
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