Author/Authors :
Alinezhad Kolaei، Adel نويسنده Shiraz University of Medical Sciences , , Javidan، Reza نويسنده Department of Computer Engineering and IT, Shiraz University of Technology , , Nematollahi، Mohtaram نويسنده School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran , , Zand، Farid نويسنده , , Nikandish، Reza نويسنده Department of Anesthesiology, Fasa University of Medical Sciences, Fasa, Iran ,
Abstract :
Introduction:APACHE (Acute Physiologic and Chronic Health Evaluation) score is a medical tool designed to measure the severity of
disease for adult patients admitted to Intensive Care Units (ICU). However, it is designed based on the American patients’ data and is not
well suited to be used for Iranian people. In addition, Iranian hospitals are not equipped with High Dependency Units which is required
for original APACHE.
Method: We aimed to design an intelligent version of APACHE system for recognition of patients’ hospitalization period in ICUs. The
new system can be designed based on Iranian local data and updated locally. Intelligence means that the system has the ability to learn
from its previous results and doesn’t need manual update.
Results: In this study, this new system is introduced and the technical specifications are presented. It is based on neural networks. It can
be trained and is capable of auto-learning. The results obtained from final implemented software show better performance than those
obtained from non-local version.
Conclusion: Using this method, the efficiency of the prediction has increased from 80% to 90%. Such results were compared with the
APACHE outputs to show the superiority of the proposed method.
