VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients
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 biosignals and their correlations is a difficult task even for expe...
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| Published in: | Scientific data Vol. 9; no. 1; pp. 279 - 9 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
08.06.2022
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2052-4463, 2052-4463 |
| Online Access: | Get full text |
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| Abstract | 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 biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine 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 contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. 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 resource for biosignal research and development.
Measurement(s)
vital signs of patients during surgery • perioperative patient information
Technology Type(s)
Vital Signs Measurement • Electronic Medical Record
Factor Type(s)
vital signs data including various numeric and waveform data acquired from multiple patient monitors • perioperative patient information acquired from the electronic medical record system
Sample Characteristic - Organism
Homo sapiens
Sample Characteristic - Environment
hospital
Sample Characteristic - Location
South Korea |
|---|---|
| AbstractList | 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 biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine 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 contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. 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 resource for biosignal research and development.Measurement(s)vital signs of patients during surgery • perioperative patient informationTechnology Type(s)Vital Signs Measurement • Electronic Medical RecordFactor Type(s)vital signs data including various numeric and waveform data acquired from multiple patient monitors • perioperative patient information acquired from the electronic medical record systemSample Characteristic - OrganismHomo sapiensSample Characteristic - EnvironmenthospitalSample Characteristic - LocationSouth Korea 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 biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine 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 contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. 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 resource for biosignal research and development. Measurement(s) vital signs of patients during surgery • perioperative patient information Technology Type(s) Vital Signs Measurement • Electronic Medical Record Factor Type(s) vital signs data including various numeric and waveform data acquired from multiple patient monitors • perioperative patient information acquired from the electronic medical record system Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment hospital Sample Characteristic - Location South Korea Measurement(s) vital signs of patients during surgery • perioperative patient information Technology Type(s) Vital Signs Measurement • Electronic Medical Record Factor Type(s) vital signs data including various numeric and waveform data acquired from multiple patient monitors • perioperative patient information acquired from the electronic medical record system Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment hospital Sample Characteristic - Location South Korea 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 biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine 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 contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. 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 resource for biosignal research and development. 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 biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine 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 contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. 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 resource for biosignal research and development. Measurement(s)vital signs of patients during surgery • perioperative patient informationTechnology Type(s)Vital Signs Measurement • Electronic Medical RecordFactor Type(s)vital signs data including various numeric and waveform data acquired from multiple patient monitors • perioperative patient information acquired from the electronic medical record systemSample Characteristic - OrganismHomo sapiensSample Characteristic - EnvironmenthospitalSample Characteristic - LocationSouth Korea 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 biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine 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 contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. 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 resource for biosignal research and development.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 biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine 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 contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. 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 resource for biosignal research and development. |
| ArticleNumber | 279 |
| Author | Park, Dongnyeok Lee, Hyung-Chul Park, Yoonsang Jung, Chul-Woo Yoon, Soo Bin Yang, Seong Mi |
| Author_xml | – sequence: 1 givenname: Hyung-Chul surname: Lee fullname: Lee, Hyung-Chul organization: Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital – sequence: 2 givenname: Yoonsang orcidid: 0000-0001-6200-2157 surname: Park fullname: Park, Yoonsang organization: Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital – sequence: 3 givenname: Soo Bin surname: Yoon fullname: Yoon, Soo Bin organization: Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital – sequence: 4 givenname: Seong Mi surname: Yang fullname: Yang, Seong Mi organization: Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital – sequence: 5 givenname: Dongnyeok surname: Park fullname: Park, Dongnyeok organization: Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital – sequence: 6 givenname: Chul-Woo orcidid: 0000-0001-7876-8659 surname: Jung fullname: Jung, Chul-Woo email: jungcwoo@gmail.com organization: Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35676300$$D View this record in MEDLINE/PubMed |
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