Volume 7, Issue 1 (6-2020)                   jhbmi 2020, 7(1): 10-19 | Back to browse issues page

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Montazeri M, Ahmadinejad M, Montazeri M, Montazeri M. Design and Implementation of a Fuzzy Intelligent System for Predicting Mortality in Trauma Patients in the Intensive Care Unit. jhbmi 2020; 7 (1) :10-19
URL: http://jhbmi.ir/article-1-360-en.html
Ph.D. Student in Medical Informatics, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
Abstract:   (3548 Views)
Introduction: The intensive care unit is one of the most costly parts of the national health sector. These costs are largely attributable to the length of stay in the intensive care unit. For this reason, there are significant benefits in predicting patients' length of stay and the percentage of deaths in intensive care units. Therefore, in this study, a fuzzy logic based intelligent system was designed to predict the percentage of deaths in trauma patients in the intensive care unit.
Method: Data needed to design the system were collected from patient files from 2010 to 2012. Then, the system was run using data collected from each file and the system diagnosis was compared with the final diagnosis recorded in the patient file. The proposed neuro-fuzzy model was compared with five other intelligent models. This comparison was calculated and evaluated based on sensitivity, accuracy, specificity, and the area under the ROC curve.
Results: The accuracy of these six models was approximately 83%, 81%, 80%, 75%, 82% and 81%, respectively.
Conclusion: The neuro-fuzzy model was evaluated as the best model and had the highest accuracy. This model also had the highest area under the ROC curve. Therefore, it is recommended to use neuro-fuzzy model to diagnose and predict the percentage of deaths in trauma patients in the intensive care unit. This is important in health-related research particularly in allocating therapeutic resources to people at risk.
Full-Text [PDF 1438 kb]   (1417 Downloads)    
Type of Study: Applicable | Subject: Artificial Intelligence in Healthcare
Received: 2018/11/21 | Accepted: 2019/12/11

Audio File [MP3 1595 KB]  (103 Download)
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