Automatic Clinical Procedure Detection for Emergency Services
Understanding a patient's state is critical to providing optimal care. However, information loss occurs during patient hand-offs (e.g., emergency services (EMS) transferring patient care to a receiving hospital), which hinders care quality. Augmenting the information flow from an EMS vehicle to a receiving hospital may reduce information loss and improve patient outcomes. Such augmentation requires a noninvasive system that can automatically recognize clinical procedures being performed and send near real-time information to a receiving hospital. An automatic clinical procedure detection system that uses wearable sensors, video, and machine-learning to recognize clinical procedures within a controlled environment is presented. The system demonstrated how contextual information and a majority vote method can substantially improve procedure recognition accuracy. Future work concerning computer vision techniques and deep learning are discussed.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2019 |
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| Enthalten in: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference - 2019(2019) vom: 15. Juli, Seite 337-340 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Heard, Jamison [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 24.02.2020 Date Revised 28.09.2020 published: Print Citation Status MEDLINE |
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| doi: |
10.1109/EMBC.2019.8856281 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM305442139 |
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| 520 | |a Understanding a patient's state is critical to providing optimal care. However, information loss occurs during patient hand-offs (e.g., emergency services (EMS) transferring patient care to a receiving hospital), which hinders care quality. Augmenting the information flow from an EMS vehicle to a receiving hospital may reduce information loss and improve patient outcomes. Such augmentation requires a noninvasive system that can automatically recognize clinical procedures being performed and send near real-time information to a receiving hospital. An automatic clinical procedure detection system that uses wearable sensors, video, and machine-learning to recognize clinical procedures within a controlled environment is presented. The system demonstrated how contextual information and a majority vote method can substantially improve procedure recognition accuracy. Future work concerning computer vision techniques and deep learning are discussed | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Paris, Richard A |e verfasserin |4 aut | |
| 700 | 1 | |a Scully, Deirdre |e verfasserin |4 aut | |
| 700 | 1 | |a McNaughton, Candace |e verfasserin |4 aut | |
| 700 | 1 | |a Ehrenfeld, Jesse M |e verfasserin |4 aut | |
| 700 | 1 | |a Coco, Joseph |e verfasserin |4 aut | |
| 700 | 1 | |a Fabbri, Daniel |e verfasserin |4 aut | |
| 700 | 1 | |a Bodenheimer, Bobby |e verfasserin |4 aut | |
| 700 | 1 | |a Adams, Julie A |e verfasserin |4 aut | |
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