Design and Implementation of a Hospital Patient Follow-up System Based on Deep Learning

With the rapid development of deep learning technology in the medical field, it has shown great potential in improving patient care and health management. This study aims to design and implement a hospital patient follow-up system based on deep learning to enhance the efficiency and quality of follow-up care. By conducting an in-depth analysis of the shortcomings of current medical follow-up systems, we propose an innovative solution that integrates the latest deep learning algorithms. The system utilizes deep learning models to analyze patient data, predict trends in their health condition, and automatically schedule follow-up appointments as needed. Additionally, the system provides personalized health advice and intervention measures aimed at improving patient treatment compliance and quality of life. Through real-world case testing, the system demonstrates significant benefits in improving follow-up efficiency, reducing medical errors, and enhancing patient satisfaction. This study not only advances the application of deep learning technology in the field of medical follow-up but also provides an important reference for the development of related systems in the future.