Implementing the EBDI model in an E-health system

Document Type : Research Paper


1 Department of Computer Science, University of Al-Qadisiyah, Al-Qadisiyah, Iraq

2 Computer Science Department, College of Computer Science and Information Technology, University of Al-Qadisiyah, Iraq


Health-care systems (patient monitoring and diagnosis systems) have recently attracted the interest of researchers. Intelligent software has been used as an agent in this field where research addresses the areas of electronic health (E-health) to develop and improve health care systems and to reduce the effort and time on health users (physicians, nurses) and to follow up on the condition of patients, especially the elderly, and also Intelligent software agents are using a result when most hospitals are out of capacity.  The major goal of this study is to learn how we may use intelligent software for  E-Health to assist physicians, nurses, and other health practitioners in collecting and tracking patient data on a daily basis in order to enhance treatment decisions. How can we achieve this in real-time in a dynamic and non-deterministic environment? As a result, we have built a model-based system such as the Extensible Beliefs Desires and Intentions (EBDI) model as architecture for located autonomic software agents in run-time under dynamic and non-deterministic environments circumstances in this study. We have formalized the EBDI agent in E-Health as an online observer to monitor the state of a  patient linked to sensors (thermometer, glucose meter, oxygen meter, etc.). This agent filters the data (sensor readings) and determines which readings are normal or abnormal before sending the patient's information to the administrative agent, which is a different type of EBDI agent. In addition, the system proposes a treatment plan based on the sensor readings and necessitates the participation of a human doctor in emergency situations. This study proposes developing a monitoring system to follow up (monitor) the health status of patients based on a multi-agent-system (MAS) with the Clustering process (using the K-mean Algorithm), where the system is designed to support warnings and alerts in abnormal health conditions and to call for a doctor's intervention in emergencies (when it is necessary).