Nursing Diagnosis in Improving Patient Outcomes: A Comprehensive Review of Current Practices and Future Directions

Author(s): Jiang Zuo

Nursing diagnosis is a critical aspect of nursing practice, and the use of computers in diagnosing patients has become increasingly popular in recent years. There are many advantages to using computerized nursing diagnosis, including increased accuracy, efficiency, and standardization of diagnoses. In this review article, we will explore the benefits of using computerized nursing diagnosis and discuss some of the challenges and limitations of this approach. 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 current feature extraction speed and recognition effect of intelligent diagnosis of menopausal women’s health care behavior, this paper proposes to use a cross-layer convolutional neural network to extract behavior features autonomously and use support vector machine multiclass behavior classifier to classify behavior. Compared with the feature images extracted by traditional methods, the behavioral features extracted in this paper are related to the individual menopausal women and have better semantic information, and the feature description ability in the time domain and the space domain has been enhanced.