PATIENT MODEL BASED PERSONALIZED REMOTE HEALTH CARE FOR CHRONIC DISEASE

Recent advancements in wearable smart devices, medical internet of things, cloud computing, wireless communications, and AI-based technologies have enabled personalized remote health care for patients with chronic diseases. Covid pandemic period has exposed the shortfall of the healthcare system, where there was a massive shortage of doctors, nurses, medical supplies, hospital beds, and other healthcare infrastructure, which has affected many patients with chronic diseases who needed constant monitoring and consultation with doctors. This has affirmed the necessity of remotely monitoring the patients to predict their requirements for medicines, treatment, etc., and to avoid any unusual severe condition. In this work, we presented the Patient Model to monitor the patient's activities and remotely identify and fulfil their treatment requirements. The framework monitors the patients and depending on the diagnosis provides personalized remote health care services such as telemedicine, medical tests, diet plans, etc., along with an ambulance facility if needed. The proposed framework uses a CNN and other machine learning algorithm to predict the required personalized healthcare service requirements. The simulation results show that the proposed framework, using the patient's model, and CNN algorithm significantly improves the precision and recall of the prediction and reduces the time to predict the requirements.