A new intelligent approach for estimation of blood potassium concentration

Abstract

This paper introduces a new approach for estimation of blood potassium concentration. This approach is based on a neurofuzzy inference system that combines the attributes of both fuzzy logic and neural networks. This approach has many attractive clinical features. First, it represents a computerised intelligent method for accurately estimating blood potassium without the need for a potassium sensor. Second, it helps the clinicians in diagnosis and treatment of potassium disorders and also reduces the time required to deal with them. Third, it enhances the patient's comfort and compliance due to a significant reduction in blood sample volume required to conduct the electrolytes analysis. Lastly, the developed approach explains the complex physiological homeostasis of blood electrolytes which is very important for the design of decision-making systems for medical applications. Furthermore, the validation results of this method showed that it is capable of estimating the blood potassium with an accuracy satisfying clinical standards.

Theoretical Estimation of Cannulation Methods for Left Ventricular Assist Device Support as a Bridge to Recovery

“…Shi et al (8) suggested a new cannulation method that is connected in series to the failed heart and used a theoretical method to show that LVAD support with the in-series cannulation method provided good performance for pumping blood to peripheral organs and ventricular unloading. However, they did not consider the effect on coronary perfusion during LVAD support or provide a direct comparison between LA-AA and the inseries method in terms of ventricular unloading effect.…”