Intravenous (IV) medication administration processes have been considered as high-risk steps, because accidents during IV administration can lead to serious adverse effects, which can deteriorate the therapeutic effect or threaten the patient’s life. In this study, we propose a multi-modal infusion pump (IP) monitoring technique, which can detect mismatches between the IP setting and actual infusion state and between the IP setting and doctor’s prescription in real time using a thin membrane potentiometer and convolutional-neural-network-based deep learning technique. During performance evaluation, the percentage errors between the reference infusion rate (IR) and average estimated IR were in the range of 0.50–2.55%, while those between the average actual IR and average estimated IR were in the range of 0.22–2.90%. In addition, the training, validation, and test accuracies of the implemented deep learning model after training were 98.3%, 97.7%, and 98.5%, respectively. The training and validation losses were 0.33 and 0.36, respectively. According to these experimental results, the proposed technique could provide improved protection functions to IV-administration patients.

静脉 (IV) 给药过程被认为是高风险步骤，因为 IV 给药过程中的意外会导致严重的不良反应，从而降低治疗效果或威胁患者的生命。在本研究中，我们提出了一种多模式输液泵 (IP) 监测技术，该技术可以使用薄膜电位器和卷积神经网络实时检测 IP 设置与实际输液状态之间以及 IP 设置与医生处方之间的不匹配情况。 -基于网络的深度学习技术。在性能评估期间，参考输注速率 (IR) 和平均估计 IR 之间的百分比误差在 0.50-2.55% 范围内，而平均实际 IR 和平均估计 IR 之间的百分比误差在 0.22-2.90% 范围内。此外，在培训中，训练后实施的深度学习模型的验证和测试准确率分别为 98.3%、97.7% 和 98.5%。训练和验证损失分别为 0.33 和 0.36。根据这些实验结果，所提出的技术可以为静脉注射患者提供更好的保护功能。