Five Years Survival of Patients After Liver Transplantation and Its Effective Factors by Neural Network and Cox Poroportional Hazard Regression Models

Abstract

Background:Transplantation is the only treatment for patients with liver failure. Since the therapy imposes high expenses to the patients and community, identification of effective factors on survival of such patients after transplantation is valuable.Objectives:The current study attempted to model the survival of patients (two years old and above) after liver transplantation using neural network and Cox Proportional Hazards (Cox PH) regression models. The event is defined as death due to complications of liver transplantation.Patients and Methods:In a historical cohort study, the clinical findings of 1168 patients who underwent liver transplant surgery (from March 2008 to march 2013) at Shiraz Namazee Hospital Organ Transplantation Center, Shiraz, Southern Iran, were used. To model the one to five years survival of such patients, Cox PH regression model accompanied by three layers feed forward artificial neural network (ANN) method were applied on data separately and their prediction accuracy was compared using the area under the receiver operating characteristic curve (ROC). Furthermore, Kaplan-Meier method was used to estimate the survival probabilities in different years.Results:The estimated survival probability of one to five years for the patients were 91%, 89%, 85%, 84%, and 83%, respectively. The areas under the ROC were 86.4% and 80.7% for ANN and Cox PH models, respectively. In addition, the accuracy of prediction rate for ANN and Cox PH methods was equally 92.73%.Conclusions:The present study detected more accurate results for ANN method compared to those of Cox PH model to analyze the survival of patients with liver transplantation. Furthermore, the order of effective factors in patients’ survival after transplantation was clinically more acceptable. The large dataset with a few missing data was the advantage of this study, the fact which makes the results more reliable.

Application of Artificial Intelligence to Gastroenterology and Hepatology

“…Konerman et al 144,145 created an ML model based on clinical, laboratory and histologic data that identified patients with chronic hepatitis C virus infection at highest risk for disease progression and liver-related outcomes (eg, liver-related death, hepatic decompensation, hepatocellular carcinoma, liver transplant, or increase in Child-Pugh score to 7) with an AUROC curve of 0.78 in a validation cohort of 1007 patients. Khosravi et al 146 developed an ANN to predict survival times of 1168 patients undergoing liver transplantation; it estimated survival probability of 1-5 years with an AUROC curve of 86.4% compared to 80.7% for Cox proportional hazard regression models. Researchers have also used ANN to match liver donors with recipients, which could provide powerful decision-making technology.…”

Unsupervised neural network for evaluating the ability of the SF-36 instrument to differentiate individuals

“…Therefore, we could find no comparable studies in the literature. However, some studies have investigated the application of artificial neural network methods in HRQoL (7)(8)(9)(10)(11)(12)(13)(14); however, their main objectives were different from those of the present study. Indeed, no one has evaluated the sensitivity and specificity of HRQoL instruments.…”

“…These methods and other similar datamining techniques are widely used in different medical fields. Some recent works include disease diagnosis (3), imaging analysis (4,5), predicting disease status (6), identification of important risk factors for disease (7), and modelling survival of patients (8). A few studies with artificial neural networks have included finding cut-off scores for HRQoL of people with incontinence problems (9), identifying heart failure (10), HRQoL in diabetes (11), HRQoL after breast cancer surgery (12), HRQoL of Parkinson's disease (13), and predicting the response to a standard 4-week interdisciplinary pain programme (14).…”

Identifying Important Attributes for Prognostic Prediction in Traumatic Brain Injury Patients

“…Owing to the uncertainty that exists in biological systems, methods for pattern recognition in clinical findings have always been of particular interest to researchers. The techniques in this study have been frequently employed in clinical research [1,3]; for example, ANN modeling methods were deployed for prediction and diagnosis in different medical studies [4][5][6][7]. In addition, for medical decision-making, DT algorithms were variously utilized [8][9][10][11].…”