Clinical validation of an artificial neural network trained to identify acute allograft rejection in liver transplant recipients

Hughes, Victoria; Melvin, David; Niranjan, Mahesan; Alexander, G A; Trull, Andrew K.

doi:10.1053/jlts.2001.24642

Cited by 20 publications

(10 citation statements)

References 38 publications

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“…It usually happens within the first month of transplantation and its diagnosis depends heavily on the use of liver biopsy. Given that liver biopsy provides pivotal information for diagnosis and serves to guide further therapeutic decisions, it is itself a highly risky procedure, particularly in patients with impaired coagulation (9,28). Thus, it is not appropriate for regular monitoring of liver transplant patients.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the development and improvement of immunosuppressive regimens and surgical techniques in the last decades, acute rejection (AR) still remains of fundamental problems in 10% -20% of liver transplant (LT) patients and it is more common in the first few weeks posttransplantation. AR episodes are distinguished in 34% to 70% of patients, and 5% to 20% of patients will result in chronic rejection (CR), which is usually irreversible and needs re-transplantation (5)(6)(7)(8)(9). Currently, liver biopsy is a widely used gold standard for the examination of the rejection in liver transplant patients.…”

Section: Introductionmentioning

confidence: 99%

“…Victoria et al also reported excellent diagnostic accuracies (AUC 0.902 with sensitivity and specificity of 80.0% and 90.1% at the optimum decision threshold, respectively) when training ANN to predict acute rejection in liver transplant recipients over the first 3 months after transplantation using alanine aminotransferase, bilirubin levels, and day posttransplantation (9). Besides, Cucchetti similarly demonstrated superiority of ANN to MELD in predicting mortality of patients with end-stage liver disease (AUC 0.95).…”

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confidence: 97%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 97%

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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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“…The computer-assisted tool based on ANN in our study predicts the treatment outcome for patients with CHC treated with PEG-IFN/RBV therapy with greater performance compared with the multiple LR model because the ANN model has the advantage of being able to be used to translate multivariate nonlinear relations into continuous functions without the need of understanding exactly the underlying relationships between variables [19,20] . Furthermore, the ANN model does not require analysis of data because the solution is self-evolving [21] . In addition, to compare the prediction accuracy between the above two models, we used ROC curve analysis in our study, which is a useful way to evaluate the performance of classification schemes where there is one variable with two categories by which subjects are classified.…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Network Model Is Superior to Logistic Regression Model in Predicting Treatment Outcomes of Interferon-Based Combination Therapy in Patients with Chronic Hepatitis C

Wang

Lin

et al. 2008

Intervirology

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Background/Aims: Patients with chronic hepatitis C (CHC) can achieve a sustained virologic response if they received pegylated interferon plus ribavirin therapy; however, some of them do not respond or relapse after treatment. The aim of this study was to compare the ability of two statistical models to predict treatment outcomes. Methods: Clinical data, biochemical values, and liver histological features of 107 patients with CHC were collected and assessed using a logistic regression (LR) model and an artificial neural network (ANN) model. Both the LR and ANN models were compared by receiver-operating characteristics curves. Results: Aspartate aminotransferase (p = 0.017), prothrombin time (p = 0.002), body mass index (BMI; p = 0.003), and fibrosis score of liver histology (p = 0.002) were found to be significant predictive factors by univariate analysis. The independent significant predicting factor was BMI by multivariate LR analysis (p = 0.0095). The area under receiver-operating characteristics of the ANN model was larger than that of the LR model (85 vs. 58.4%). Conclusions: It was found that BMI is an independent factor for identifying patients with favorable treatment response. A useful ANN model in predicting outcomes of standard treatment for CHC infection was developed and showed greater accuracy than the LR model.

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“…The MLP architecture combines layers of perceptron-like processing elements (the neurons) connected by weighted connections (the synapses) to produce a network capable of dealing with complex nonlinearly separable mappings 29 . The distributed nature of the processing which takes place in a neural network contributes to the robustness of the system 30 , 31 …”

Section: Architecturementioning

confidence: 99%

Biologically inspired intelligent decision making

2013

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Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems.

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Clinical validation of an artificial neural network trained to identify acute allograft rejection in liver transplant recipients

Cited by 20 publications

References 38 publications

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

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

Artificial Neural Network Model Is Superior to Logistic Regression Model in Predicting Treatment Outcomes of Interferon-Based Combination Therapy in Patients with Chronic Hepatitis C

Biologically inspired intelligent decision making

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