In Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
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.
Hwang Young Jun, Kim Gun Ho, Sung Eui Suk, Nam Kyoung Won
Infusion pump, convolutional neural network, monitoring, patient safety, real-time