Mathematics Teaching and Research Section, Qiqihar Medical University, Qiqihar, 161000, China.
Teaching and Research Section of Computer Science, Qiqihar Medical University, Qiqihar 161000, China.
Comput Math Methods Med. 2022 Jan 28;2022:4646454. doi: 10.1155/2022/4646454. eCollection 2022.
This research was aimed at exploring the application value of a mobile medical management system based on Internet of Things technology and medical data collection in stroke disease prevention and rehabilitation nursing. In this study, on the basis of radio frequency identification (RFID) technology, the signals collected by the sensor were filtered by the optimized median filtering algorithm, and a rehabilitation nursing evaluation model was established based on the backpropagation (BP) neural network. The performance of the medical management system was verified in 32 rehabilitation patients with hemiplegia after stroke and 6 healthy medical staff in the rehabilitation medical center of the hospital. The results showed that the mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the median filtering algorithm after optimization were significantly higher than those before optimization ( < 0.05). When the number of neurons was 23, the prediction accuracy of the test set reached a maximum of 89.83%. Using traingda as the training function, the model had the lowest training time and root mean squared error (RMSE) value of 2.5 s and 0.29, respectively, which were significantly lower than the traingd and traingdm functions ( < 0.01). The error percentage and RMSE of the model reached a minimum of 7.56% and 0.25, respectively, when the transfer functions of both the hidden and input layers were tansig. The prediction accuracy in stages III~VI was 90.63%. It indicated that the mobile medical management system established based on Internet of Things technology and medical data collection has certain application value for the prevention and rehabilitation nursing of stroke patients, which provides a new idea for the diagnosis, treatment, and rehabilitation of stroke patients.
本研究旨在探讨基于物联网技术和医疗数据采集的移动医疗管理系统在脑卒中疾病预防和康复护理中的应用价值。本研究在射频识别（RFID）技术的基础上，采用优化后的中值滤波算法对传感器采集的信号进行滤波，建立基于反向传播（BP）神经网络的康复护理评价模型。在医院康复医学中心对 32 例脑卒中偏瘫康复患者和 6 名健康医护人员进行了医疗管理系统性能验证。结果表明，优化后的中值滤波算法的均方误差（MSE）和峰值信噪比（PSNR）明显高于优化前（ < 0.05）。当神经元数量为 23 时，测试集的预测准确率达到最大值 89.83%。使用 traingda 作为训练函数，模型的训练时间和均方根误差（RMSE）值最低，分别为 2.5s 和 0.29，显著低于 traingd 和 traingdm 函数（ < 0.01）。当隐层和输入层的传递函数均为 tansig 时，模型的误差百分比和 RMSE 分别达到最小值 7.56%和 0.25。在 III~VI 阶段的预测准确率为 90.63%。这表明，基于物联网技术和医疗数据采集的移动医疗管理系统在脑卒中患者的预防和康复护理中具有一定的应用价值，为脑卒中患者的诊断、治疗和康复提供了新的思路。
