An Intelligent Rule-based System for Status Epilepticus Prognostication
محل انتشار: مجله فیزیک و مهندسی پزشکی، دوره: 11، شماره: 2
سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 57
فایل این مقاله در 12 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JBPE-11-2_008
تاریخ نمایه سازی: 30 دی 1402
چکیده مقاله:
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.
کلیدواژه ها:
نویسندگان
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
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :