Developing a Clinical Decision Support System for Prediction Postoperative Coronary Artery Bypass Grafting Infection in Diabetic Patients
- سال انتشار: 1401
- محل انتشار: مجله فیزیک و مهندسی پزشکی، دوره: 12، شماره: 6
- کد COI اختصاصی: JR_JBPE-12-6_005
- زبان مقاله: انگلیسی
- تعداد مشاهده: 50
نویسندگان
PhD, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
PhD, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
PhD, Department of Health Information Technology, School of Allied Medical Sciences, Lorestan University of Medical Sciences, Khorramabad, Iran
MSc, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
PhD, Amol Faculty of Paramedical Sciences, Mazandaran University of Medical Sciences, Sari, Iran
MD, Department of Cardiology, Pars Hospital, Tehran, Iran
MSc, Department of Robotic Engineering, Shahrood University of Technology, Shahrood, Iran
چکیده
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 ۲۰۱۶. From ۱۰۶۱ CABG surgery medical records, we selected ۲۱۰ 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 constructed 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 ۲۸ variables in the data, ۲۰ variables had a significant relationship with infection after CABG (P< ۰.۰۵). The results of the confusion matrix showed that the sensitivity of the system was ۶۹%, and the specificity and the accuracy were ۹۷% and ۸۴%, 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 supporting 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.کلیدواژه ها
Decision Support Systems, Clinical, Surgical Wound Infection, Coronary artery bypass, Diabetesاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.