Design and Application of Hospital Medical Network Structure and Intelligent Patient Monitoring Based on Machine Learning

Aiming at the problems of low transmission efficiency, high power consumption and security in the medical network structure. In this paper, a machine learning model is applied to hospital medical network structure and patient monitoring intelligent management. The results show that the machine learning algorithm has a good network lifetime for the extraction of data from the database when the number of nodes reaches 500, the network lifetime can reach 29[Formula: see text]s, and the machine learning algorithm consumes less energy and will not repeat the query of the data in the database. The delay of the machine learning algorithm will be applicable to all kinds of emergencies in the intelligent medical system, and the delay is also less than other models, which have high application value. The monitoring of patients is realized by studying the change of acceleration. Through the experimental results of this paper, it can be found that the change trend of acceleration under normal walking, and rising and falling postures of patients is very different. Under normal circumstances, the change trend of acceleration is always maintained in a stable range. When falling, the change trend of acceleration is larger, the highest can reach 2.8. Thereby realizing the judgment of the posture of the patient and the implementation of monitoring and having a higher success rate.