Reinforcement Learning-Enhanced Telemedicine Platform for Atrial Fibrillation Medication Regimens and Personalized Recommendations

Through the use of machine learning and advanced data analysis techniques, this paper plans to identify a concept for a new telemedicine-related platform with a focal point on atrial fibrillation and personalized healthcare regarding it. As of now, machine learning exists in a wide variety. Example techniques range from commonly used support vector machines (SVMs) to the most complex deep learning algorithms. However, all algorithms coincide when it comes to getting the most accuracy possible, which is exactly what is done with a reinforcement learning model. This study utilizes reinforcement learning to train massive amounts of data, maximizing the amount of accuracy possible. We intend to use both real-time data (collected through the user of the platform) and historical data (from open-access databases like PhysioNet, OpenNeuro, and OpenfMRI. The final goal is to not just create a platform, but one that has gone through extensive training and multiple clinical trials, just to make sure that this model can be safely implemented in the future. To expand on future purposes, we hope that multiple different types of medical organizations can successfully integrate this software for their daily use, increasing the scope of healthcare and its collaboration between many different fields and expertise.