Emergency Department, Zhejiang Hospital, Hangzhou, Zhejiang 310030, China.
Neurosurgery Department, Zhejiang Hospital, Hangzhou, Zhejiang 310030, China.
J Healthc Eng. 2021 Oct 6;2021:1034972. doi: 10.1155/2021/1034972. eCollection 2021.
In order to help pathologists quickly locate the lesion area, improve the diagnostic efficiency, and reduce missed diagnosis, a convolutional neural network algorithm for the optimization of emergency nursing rescue efficiency of critical patients was proposed. Specifically, three convolution layers and convolution kernels of different sizes are used to extract the features of patients' posture behavior, and the classifier of patients' posture behavior recognition system is used to learn the feature information by capturing the nonlinear relationship between the features to achieve accurate classification. By testing the accuracy of patient posture behavior feature extraction, the recognition rate of a certain action, and the average recognition rate of all actions in the patient body behavior recognition system, it is proved that the convolution neural network algorithm can greatly improve the efficiency of emergency nursing. The algorithm is applied to the patient posture behavior detection system, so as to realize the identification and monitoring of patients and improve the level of intelligent medical care. Finally, the open source framework platform is used to test the patient behavior detection system. The experimental results show that the larger the test data set is, the higher the accuracy of patient posture behavior feature extraction is, and the average recognition rate of patient posture behavior category is 97.6%, thus verifying the effectiveness and correctness of the system, to prove that the convolutional neural network algorithm has a very large improvement of emergency nursing rescue efficiency.
为帮助病理医生快速定位病变区域，提高诊断效率，减少漏诊，提出一种优化危急患者急诊护理抢救效率的卷积神经网络算法。具体来说，使用三个卷积层和不同大小的卷积核提取患者姿态行为的特征，并通过捕获特征之间的非线性关系来学习患者姿态行为识别系统的分类器的特征信息，以实现准确分类。通过测试患者姿态行为特征提取的准确性、某个动作的识别率以及患者身体行为识别系统中所有动作的平均识别率，证明卷积神经网络算法可以极大地提高急诊护理的效率。将该算法应用于患者姿态行为检测系统，实现对患者的识别和监测，提高智能医疗水平。最后，使用开源框架平台对患者行为检测系统进行测试。实验结果表明，测试数据集越大，患者姿态行为特征提取的准确率越高，患者姿态行为类别平均识别率达到 97.6%，从而验证了系统的有效性和正确性，证明了卷积神经网络算法对急诊护理抢救效率有很大的提升。
