Real-time COPD-based Clinical Data Management using Machine Learning

Abstract

Our research focuses on using big data technology with a structured and personalized nursing care approach to improve treatment for patients with COPD and respiratory arrest. This study evaluates a continuous care strategy for COPD and RF therapies using big data. The approach reduces COPD and RF severity and flare-up frequency, duration, and number to improve clients’ quality of life. A prominent university hospital’s lung health system included 100 COPD and RF patients. They were randomly assigned to the control or experimental group. A department called control group patients first. After the consultation, patients received answers to their issues. Trial participants received 24/7 care through the internet and big data analytics. The unit’s first nursing care anchored these activities. Health, fitness, function evaluation, and rehabilitation goals can be customized for each patient. Individualized therapy using big data technologies improves outcomes for stable COPD and RF patients, including lung capacity, frequency of acute episodes, readmission rates, self-management, and quality of life. This strategy may be useful in a nursing home where the patient receives constant care.