Learning Techniques for Personalized Oxygen Therapy in Pulmonary Rehabilitation Using Cloud Infrastructure

Personalized oxygen treatment is essential to maximize pulmonary rehabilitation (PR) for individuals with long-term respiratory disorders. This research offers a unique method to improve oxygen treatment customization by integrating advanced deep learning techniques within cloud-based architecture, particularly Long Short-Term Memory (LSTM) networks. To allow for personalized oxygen treatment changes, the proposed solution combines LSTM networks with cloud computing capabilities to collect and evaluate large amounts of health data and indicators from real-time monitoring. With LSTM networks, complicated temporal patterns in patient data may be captured and learned, leading to more accurate treatment predictions and changes. Treatment plans are continuously modified depending on individual patient demands and changing health situations; the system provides scalable and adaptable solutions for ongoing therapy optimization using cloud infrastructure. A battery of tests contrasting the device with conventional oxygen treatment techniques proves its efficacy. The results significantly improve the accuracy of predictions, treatment classification, and patient adherence. An effective, scalable, and patient-centered solution for controlling oxygen treatment in varied clinical settings may be achieved by merging deep learning with cloud technology; our study highlights the revolutionary potential of this combination in pulmonary rehabilitation. LSTM networks combined with cloud computing provide a huge step forward in individualized medicine, which may contribute to better results for chronic respiratory disease patients.