Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 6
• Main Authors: Ma, Xiaojun, Zhang, Zhenfeng
• Format: Journal Article
• Language: English
• Published: United States Hindawi 25.06.2022; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Analysis; Belief networks; Big Data; Computers; Control equipment; Control systems; Data analysis; Data mining; Data processing; Deep learning; Failure; Fault diagnosis; Fault location; Feature extraction; Feedback; Hospitals; Human-computer interaction; Information management; Learning algorithms; Machine Learning; Medical advice systems; Medical equipment; Medical research; Multidimensional methods; Multilayers; Neural networks; Neural Networks, Computer; Perception; Physiological apparatus; Quality control; Rehabilitation; Simulation; Software; Sports; Sports medicine; Tertiary Care Centers; Trends
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/4219976

Objective. The processing and analysis of medical rehabilitation information data in tertiary hospitals is a hot research topic. Combining medical data analysis with machine learning algorithms to improve data mining efficiency is a problem that needs to be solved at present. This paper proposes an autonomous perception model of sports medicine rehabilitation equipment based on a deep learning algorithm for sports medical rehabilitation data. Methods. This paper cites a deep learning multi-dimensional perception model for medical rehabilitation equipment autonomous perception. The model utilizes the automatic overhaul of medical rehabilitation equipment based on deep belief networks. This paper extracts features through a multi-layer neural network and obtains fault location results of medical rehabilitation equipment through softmax. Results. In similarity prediction, the accuracy rate of the first three kinds of feedback containing the target answer is 77%. The accuracy rate of the target answers included in the top five kinds of feedback was 92%. Conclusion. In this study, it is feasible to apply deep learning to the quality control information system of sports rehabilitation medical equipment. This improves the management efficiency of medical rehabilitation equipment to a certain extent.