Li Lingfeng, Paris Richard A, Pinson Conner, Wang Yan, Coco Joseph, Heard Jamison, Adams Julie A, Fabbri Daniel V, Bodenheimer Bobby
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:158-163. doi: 10.1109/EMBC44109.2020.9175575.
Information about a patient's state is critical for hospitals to provide timely care and treatment. Prior work on improving the information flow from emergency medical services (EMS) to hospitals demonstrated the potential of using automated algorithms to detect clinical procedures. However, prior work has not made effective use of video sources that might be available during patient care. In this paper we explore the use convolutional neural networks (CNNs) on raw video data to determine how well video data alone can automatically identify clinical procedures. We apply multiple deep learning models to this problem, with significant variation in results. Our findings indicate performance improvements compared to prior work, but also indicate a need for more training data to reach clinically deployable levels of success.
患者状态信息对于医院及时提供护理和治疗至关重要。先前关于改善从紧急医疗服务（EMS）到医院的信息流的工作表明，使用自动算法检测临床程序具有潜力。然而，先前的工作尚未有效利用患者护理期间可能可用的视频源。在本文中，我们探索了在原始视频数据上使用卷积神经网络（CNN），以确定仅视频数据能在多大程度上自动识别临床程序。我们将多个深度学习模型应用于此问题，结果有显著差异。我们的研究结果表明与先前工作相比性能有所提升，但也表明需要更多训练数据才能达到临床可部署的成功水平。
