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Developing a Clinical Decision Support System for Prediction Postoperative Coronary Artery Bypass Grafting Infection in Diabetic Patients Publisher



Ghazisaeedi M1 ; Shahmoradi L1 ; Garavand A2 ; Maleki M1 ; Abhari S3 ; Ladan M4 ; Mehdizadeh S5
Authors
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Authors Affiliations
  1. 1. Department of Health Information Man-agement, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Department of Health Information Tech-nology, School of Allied Medical Sciences, Lorestan University of Medical Sciences, Khorramabad, Iran
  3. 3. Amol Faculty of Paramedical Sciences, Mazandaran University of Medical Sciences, Sari, Iran
  4. 4. Department of Car-diology, Pars Hospital, Tehran, Iran
  5. 5. Department of Ro-botic Engineering, Shah-rood University of Technology, Shahrood, Iran

Source: Journal of Biomedical Physics and Engineering Published:2022


Abstract

Background: Postoperative infection in Coronary Artery Bypass Graft (CABG) is one of the most common complications for diabetic patients, due to an increase in the hospitalization and cost. To address these issues, it is necessary to apply some solutions. Objective: The study aimed to the development of a Clinical Decision Support System (CDSS) for predicting the CABG postoperative infection in diabetic patients. Material and Methods: This developmental study is conducted on a private hospital in Tehran in 2016. From 1061 CABG surgery medical records, we selected 210 cases randomly. After data gathering, we used statistical tests for selecting related features. Then an Artificial Neural Network (ANN), which was a one-layer perceptron network model and a supervised training algorithm with gradient descent, was con-structed using MATLAB software. The software was then developed and tested using the receiver operating characteristic (ROC) diagram and the confusion matrix. Results: Based on the correlation analysis, from 28 variables in the data, 20 variables had a significant relationship with infection after CABG (P<0.05). The results of the confusion matrix showed that the sensitivity of the system was 69%, and the specificity and the accuracy were 97% and 84%, respectively. The Receiver Operating Characteristic (ROC) diagram shows the appropriate performance of the CDSS. Conclusion: The use of CDSS can play an important role in predicting infection after CABG in patients with diabetes. The designed software can be used as a support-ing tool for physicians to predict infections caused by CABG in diabetic patients as a susceptible group. However, other factors affecting infection must also be considered for accurate prediction. © 2022, Shiraz University of Medical Sciences. All rights reserved.