Prediction of Hypotension in Hemodialysis Sessions

Abstract

There are 4.35 million people who receive the hemodialysis treatment or undergoing a transplant as they had suffered from the session’s duration or the session’s complications. Therefore, this research aims to solve a daily nephrology problem. The main problem is that how a hemodialysis patient suffers for several hours multiple time per week and may have complications during the session which may lead to severe risks. Hypotension is one of the critical problems that faces any dialysis patient where there is a sudden drop in the patient’s blood pressure. According to nephrologists and previous research papers showed how difficult it was to treat and detect the occurrence of hypotension. In addition to, hypotension occurrence has consequences that may lead to death. Therefore, hypotension should be rapidly recognized to avoid any tragic consequences. To achieve this target, a few steps were needed. The research claims to have different approach to handle the hypotension. The first approach is hypotension prediction. Firstly, the data set is required to be from dialysis session to monitor the patient records from the dialysis machine. After preparing the artificial neural network (ANN) model to be able to predict, it needs some adjustment to make it efficient to use and a powerful model to handle the intradialytic event. The second approach is how to handle the occurrence of hypotension through the session and to decide as soon as the blood pressure drops. Therefore, the fuzzy control system is considered as one of the powerful tools to handle similar scenarios as it’s considered as biofeedback system. The second approach depends on different dataset and only few inputs to decide the blood filtration rate of the hemodialysis machine. In conclusion, the 2 approaches aim to handle a major problem that occurs daily in the dialysis units around the world.