Abstract:
The timely delivery of care and treatment in hospitals relies on accurate patient status information. Previous studies have explored the use of automated algorithms to detect clinical operations, particularly in enhancing the information flow from emergency medical services (EMS) to hospitals. However, these studies have not effectively utilized video sources that are available during patient care. In this research, we investigate the effectiveness of using convolutional neural networks (CNNs) on raw video footage to detect clinical procedures. We employ various deep learning models and evaluate their performance. Our results demonstrate improved performance compared to previous research, although they also highlight the need for more training data to achieve levels of effectiveness suitable for clinical settings.
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