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Bagheri Lankarani, Kamran, Honarvar, Behnam, Shafi pour, Farshad, Bagherpour, Morteza, Erjaee, Asma, Rouhezamin, Mohammad Reza, Khorrami, Mojdeh, Amiri Zadeh Fard, Saeid, Seifi, Vahid, Geramizadeh, Bita, Salahi, Heshmatollah, Nikeghbalian, Saman, Shamsaeefar, Alireza, Malek-hosseini, Seyed Ali, Shirzadi, Saeedreza. (1401). Predictors of Death in the Liver Transplantation Adult Candidates: An Artificial Neural Networks and Support Vector Machine Hybrid-Based Cohort Study. سامانه مدیریت نشریات علمی, 12(6), 591-598. doi: 10.31661/jbpe.v0i0.2010-1212
Kamran Bagheri Lankarani; Behnam Honarvar; Farshad Shafi pour; Morteza Bagherpour; Asma Erjaee; Mohammad Reza Rouhezamin; Mojdeh Khorrami; Saeid Amiri Zadeh Fard; Vahid Seifi; Bita Geramizadeh; Heshmatollah Salahi; Saman Nikeghbalian; Alireza Shamsaeefar; Seyed Ali Malek-hosseini; Saeedreza Shirzadi. "Predictors of Death in the Liver Transplantation Adult Candidates: An Artificial Neural Networks and Support Vector Machine Hybrid-Based Cohort Study". سامانه مدیریت نشریات علمی, 12, 6, 1401, 591-598. doi: 10.31661/jbpe.v0i0.2010-1212
Bagheri Lankarani, Kamran, Honarvar, Behnam, Shafi pour, Farshad, Bagherpour, Morteza, Erjaee, Asma, Rouhezamin, Mohammad Reza, Khorrami, Mojdeh, Amiri Zadeh Fard, Saeid, Seifi, Vahid, Geramizadeh, Bita, Salahi, Heshmatollah, Nikeghbalian, Saman, Shamsaeefar, Alireza, Malek-hosseini, Seyed Ali, Shirzadi, Saeedreza. (1401). 'Predictors of Death in the Liver Transplantation Adult Candidates: An Artificial Neural Networks and Support Vector Machine Hybrid-Based Cohort Study', سامانه مدیریت نشریات علمی, 12(6), pp. 591-598. doi: 10.31661/jbpe.v0i0.2010-1212
Bagheri Lankarani, Kamran, Honarvar, Behnam, Shafi pour, Farshad, Bagherpour, Morteza, Erjaee, Asma, Rouhezamin, Mohammad Reza, Khorrami, Mojdeh, Amiri Zadeh Fard, Saeid, Seifi, Vahid, Geramizadeh, Bita, Salahi, Heshmatollah, Nikeghbalian, Saman, Shamsaeefar, Alireza, Malek-hosseini, Seyed Ali, Shirzadi, Saeedreza. Predictors of Death in the Liver Transplantation Adult Candidates: An Artificial Neural Networks and Support Vector Machine Hybrid-Based Cohort Study. سامانه مدیریت نشریات علمی, 1401; 12(6): 591-598. doi: 10.31661/jbpe.v0i0.2010-1212

Predictors of Death in the Liver Transplantation Adult Candidates: An Artificial Neural Networks and Support Vector Machine Hybrid-Based Cohort Study

Journal of Biomedical Physics and Engineering
دوره 12، شماره 6، بهمن و اسفند 2022، صفحه 591-598    اصل مقاله (964.92 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2010-1212
نویسندگان
Kamran Bagheri Lankarani1؛ Behnam Honarvar* 1؛ Farshad Shafi pour2؛ Morteza Bagherpour2؛ Asma Erjaee3؛ Mohammad Reza Rouhezamin4؛ Mojdeh Khorrami1؛ Saeid Amiri Zadeh Fard5؛ Vahid Seifi1؛ Bita Geramizadeh6؛ Heshmatollah Salahi7؛ Saman Nikeghbalian7؛ Alireza Shamsaeefar7؛ Seyed Ali Malek-hosseini7؛ Saeedreza Shirzadi1، 8
1MD, Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
2PhD, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
3MD, Department of Pediatrics, Shiraz University of Medical Sciences, Shiraz, Iran
4MD, Trauma Research Center, Rajaei Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
5MD, Department of Internal Medicine, Gastroenterology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
6MD, Department of Pathology, Transplant Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
7MD, Shiraz Organ Transplant Center, Shiraz University of Medical Sciences Shiraz, Iran
8MD, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
چکیده
Background: Model for end-stage liver disease (MELD) is currently used for liver transplantation (LT) allocation, however, it is not a sufficient criterion. 
Objective: This current study aims to perform a hybrid neural network analysis of different data, make a decision tree and finally design a decision support system for improving LT prioritization.
Material and Methods: In this cohort follow-up-based study, baseline characteristics of 1947 adult patients, who were candidates for LT in Shiraz Organ Transplant Center, Iran, were assessed and followed for two years and those who died before LT due to the end-stage liver disease were considered as dead cases, while others considered as alive cases. A well-organized checklist was filled for each patient. Analysis of the data was performed using artificial neural networks (ANN) and support vector machines (SVM). Finally, a decision tree was illustrated and a user friendly decision support system was designed to assist physicians in LT prioritization. 
Results: Between all MELD types, MELD-Na was a stronger determinant of LT candidates’ survival. Both ANN and SVM showed that besides MELD-Na, age and ALP (alkaline phosphatase) are the most important factors, resulting in death in LT candidates. It was cleared that MELD-Na <23, age <53 and ALP <257 IU/L were the best predictors of survival in LT candidates. An applicable decision support system was designed in this study using the above three factors.  
Conclusion: Therefore, Meld-Na, age and ALP should be used for LT allocation. The presented decision support system in this study will be helpful in LT prioritization by LT allocators.
کلیدواژه‌ها
Prioritization؛ Allocation؛ Artificial Neural Network؛ Decision Trees؛ MELD-Na؛ Liver Transplantation؛ Neural Network Computers
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مراجع
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