THE HEALTH MONITORING ANALYSIS OF PATIENTS WITH CHRONIC OBSTRUCTIVE PULMONARY DISEASE USING DEEP LEARNING

It aimed to explore the application of deep learning (DL) in health monitoring and management of chronic obstructive pulmonary disease (COPD) patients based on sport psychology. A total of 100 COPD patients, treated in Luoyang Hospital from March 2020 to March 2021, were collected. The patients were randomly divided into experimental group (Exp group) and control group (Ctrl group), with 50 cases in each group. Based on DL technology, a health intervention system for COPD patients was developed. The system mainly included seven parts: screening and risk stratification, intervention target locking, sports event generation, sports parameter calculation, sports prescription creation, prescription preparation and distribution, intervention program performance, and feedback adjustment. After that, all patients received the intervention for several weeks. The Exp group received intervention under the health monitoring robot system, while the Ctrl group only received general health consultation without health management intervention. Meanwhile, all patients received regular pulmonary computerized tomography (CT) examination during the whole experiment. The changes of blood pressure (BP), blood lipid, anxiety, depression, quality of life (QoL) and CT imaging were contrasted before and after intervention. Compared with the existing models, the proposed system had better accuracy, sensitivity, and specificity. The detection accuracy of the specific data model was similar to that of the empirical method, but the detection time was lower ([Formula: see text]). After intervention, the indices and CT imaging manifestations of the Exp group were better than those of the Ctrl group. In conclusion, the proposed health monitoring robot system for patients with COPD based on sports psychology, DL, Internet of Things (IoT), and CT technology has high application value in clinical practice.