Bahare Danaei - MSc, Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran
Reza Javidan - PhD, Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran
Maryam Poursadeghfard - MD, Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
Mohtaram Nematollahi - PhD, Department of Health Information Management, Shiraz University of Medical Sciences, Shiraz, Iran

Introduction: Status epilepticus is one of the most common emergency neurological conditions with high morbidity and mortality. The study aims is to propose an intelligent approach to determine prognosis and the most common causes and outcomes based on clinical symptoms.Material and Methods: A perceptron artificial neural network was used to predict the outcome of patients with status epilepticus on discharge. But this method, which is understandable, is known as black boxes. Therefore, some rules were extracted from it in this study. The case study of this paper is data of Nemazee hospital’s patients.Results: The proposed model was prognosticated with ۷۰% accuracy, while Bayesian network and Random Forest approaches have ۵۱% and ۴۶% accuracy. According to the results, recovery and mortality groups had often used phenytoin and anesthetic drugs as seizure controlling drug, respectively. Moreover, drug withdrawal and cerebral infarction were known as the most common etiology for recovery and mortality groups, respectively and there was a relationship between age and outcome, like as previous studies.Conclusion: To identify some factors affecting the outcome such as withdrawal, their effects either can be avoided or can use sensitive treatment for patients with poor prognosis.

Intelligent Approaches, Data mining, Artificial neural networks, Rule Based Systems, Status Epilepticus, Prognosis