A Novel Method for Diagnosing Cirrhosis in Patients with Chronic Hepatitis B: Artificial Neural Network Approach

Raoufy, Mohammad Reza; Vahdani, Parviz; Alavian, Seyed Moayed; Fekri, Sahba; Eftekhari, Parivash; Gharibzadeh, Shahriar

doi:10.1007/s10916-009-9348-8

Cited by 26 publications

(27 citation statements)

References 13 publications

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“…On the other hand, in some articles the performance differences between two LR and ANN models were discussed in which ANN showed a significantly better performance (12, 33). Considering all these cases, an ANN model was designed which is a non-linear statistical data modeling tool.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Outcomes of Combination Therapy in Patients With Chronic Hepatitis C Using Artificial Neural Network

et al. 2014

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Background:Treatment with Peginterferon Alpha-2b plus Ribavirin is the current standard therapy for chronic hepatitis C (CHC). However, many host related and viral parameters are associated with different outcomes of combination therapy.Objectives:The aim of this study was to develop an artificial neural network (ANN) model to predetermine individual responses to therapy based on patient’s demographics and laboratory data.Patients and Methods:This case-control study was conducted in Tehran, Iran, on 139 patients divided into sustained virologic response (SVR) (n = 50), relapse (n = 50) and non-response (n = 39) groups according to their response to combination therapy for 48 weeks. The ANN was trained 300 times (epochs) using clinical data. To test the ANN performance, the part of data that was selected randomly and not used in training process was entered to the ANN and the outputs were compared with real data.Results:Hemoglobin (P < 0.001), cholesterol (P = 0.001) and IL-28b genotype (P = 0.002) values had significant differences between the three groups. Significant predictive factor(s) for each group were hemoglobin for SVR (OR: 1.517; 95% CI: 1.233-1.868; P < 0.001), IL-28b genotype for relapse (OR: 0.577; 95% CI: 0.339-0.981; P = 0.041) and hemoglobin (OR: 0.824; 95% CI: 0.693-0.980; P = 0.017) and IL-28b genotype (OR: 2.584; 95% CI: 1.430-4.668;P = 0.001) for non-response. The accuracy of ANN to predict SVR, relapse and non-response were 93%, 90%, and 90%, respectively.Conclusions:Using baseline laboratory data and host characteristics, ANN has been shown as an accurate model to predict treatment outcome, which can lead to appropriate decision making and decrease the frequency of ineffective treatment in patients with chronic hepatitis C virus (HCV) infection.

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Section: Discussionmentioning

confidence: 99%

Predicting the Outcomes of Combination Therapy in Patients With Chronic Hepatitis C Using Artificial Neural Network

et al. 2014

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“…The number of hospitals that have an electronic medical record is growing rapidly [19]. On the other hand, the ferritin and iron serum are not available in all hospitals.…”

Section: Discussionmentioning

confidence: 99%

“…We determined the number of the network layers, hidden neurons and the stopping criteria via trial-and-errors process, because no commonly accepted theory exists for determining the optimum number of neurons in the hidden layer [17,18]. Generally, the transfer functions are sigmoidal, hyperbolic tangent, or linear function, of which the sigmoidal function is the most widely used [19]. In this study the tansig and purelin functions were used for hidden layers and output layer respectively.…”

Section: Effective Variablesmentioning

confidence: 99%

Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data

2011

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Iron deficiency anemia (IDA) is the most common nutritional deficiency worldwide. Measuring serum iron is time consuming, expensive and not available in most hospitals. In this study, based on four accessible laboratory data (MCV, MCH, MCHC, Hb/RBC), we developed an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) to diagnose the IDA and to predict serum iron level. Our results represent that the neural network analysis is superior to ANFIS and logistic regression models in diagnosing IDA. Moreover, the results show that the ANN is likely to provide an accurate test for predicting serum iron levels with high accuracy and acceptable precision.

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“…Nevertheless, being invasive, difficult, and costly on one hand and accompanying by significant morbidity and low mortality on the other hand are the drawbacks of this method. Thus, presenting a non-invasive, accurate, simple, and inexpensive method for detecting AR following LT can be a useful adjunct (6,(10)(11)(12). Recent studies have shown that artificial neural network (ANN) analysis is potentially more reliable than traditional statistical models are in predicting clinical outcomes (10,12,13).…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Approach to Predict Acute Allograft Rejection in Liver Transplant Recipients Using Routine Laboratory Data

Zare

Zarei

et al. 2017

Hepat Mon

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Background: Discovery of non-invasive methods for acute rejection in liver transplant patients would contribute to preservation of liver function in the graft. Recently, however, outcome prediction based on biostatistical models like artificial neural networks (ANNs) is increasingly becoming impressive in medicine. Objectives: The aim of this study was to obtain a predictive model based on ANN technique and to figure out the best time for early prediction of acute allograft rejection after transplantation in liver transplant recipients. Methods: Feed-forward, back-propagation neural network was developed to predict acute rejection in liver transplant recipients using clinical and biochemical data from 148 liver transplant recipients over days 3, 7, and 14 post-transplantation. Sensitivity and receiver-operating characteristic (ROC) analysis were done to reveal the importance of input variables and the performance of the neural network. Results:The results were compared with a logistic regression (LR) model using the same data. Our results showed that the data related to day 7 gave the best results in terms of ANN performance; and the most important factors in the predictive model were aspartate aminotransferase (AST) and alanine aminotransferase (ALT). The ANN's accuracy was 90%, sensitivity was 87%, specificity was 90% in the testing set, and the performance of the ANN was better than that of the LR model. The ANN recognized correctly eight out of ten acute rejection patients and 34 out of 36 non-rejection ones in the testing set. Conclusions: This study suggests that ANN could be a valuable adjunct to conventional liver function tests for monitoring liver transplant recipients in the early postoperative period.

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A Novel Method for Diagnosing Cirrhosis in Patients with Chronic Hepatitis B: Artificial Neural Network Approach

Cited by 26 publications

References 13 publications

Predicting the Outcomes of Combination Therapy in Patients With Chronic Hepatitis C Using Artificial Neural Network

Predicting the Outcomes of Combination Therapy in Patients With Chronic Hepatitis C Using Artificial Neural Network

Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data

A Neural Network Approach to Predict Acute Allograft Rejection in Liver Transplant Recipients Using Routine Laboratory Data

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