Artificial neural networks within medical decision support systems

Sharpe, Peter K.; Caleb, P.

doi:10.3109/00365519409088571

Cited by 15 publications

(9 citation statements)

References 11 publications

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“…Neural networks lack centralized control in the classical sense, since all the interconnected processing elements change or ''adapt'' simultaneously with the flow of information and adaptive rules [41][42][43]. Several reviews on ANN have been published recently [41,[43][44][45][46][47][48][49][50]. Although there are several types of neural networks, multilayer perceptron is the most commonly used network.…”

Section: What Is An Artificial Neural Network?mentioning

confidence: 99%

“…Artificial neural network have been used in medicine for varied purposes, including prediction of mortality of patients with cirrhosis of the liver [35,47,51,52]. We compared an ANN model with Child-Pugh's scoring and a conventional logistic regression model based on the same variables that were used for the ANN model to predict 1-year liver disease-related mortality in 154 patients with cirrhosis of the liver [35].…”

Section: Review Of Studies On Ann For Prediction Of the Outcome Of Chmentioning

confidence: 99%

“…Such models will also need to be validated prospectively for use in decision making regarding organ allocation in liver transplantation centers [41,47]. Since only one study has been published till date comparing MELD score with ANN-based models for prediction of mortality from cirrhosis of the liver, such studies are urgently needed.…”

Section: Future Directionsmentioning

confidence: 99%

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Models for prediction of mortality from cirrhosis with special reference to artificial neural network: a critical review

Ghoshal

Das

2007

Hepatol Int

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Prediction of mortality of patients with cirrhosis of liver, a common and potentially fatal disease, is important for timely listing of patients for liver transplantation. The Child-Pugh scoring system has been widely used for predicting the outcome of liver cirrhosis. The Model for End-Stage Liver Disease (MELD) score has recently become popular for prediction of short-term mortality for organ allocation. A few studies that evaluated artificial neural network (ANN)-based model for prediction of outcome of cirrhosis of liver in terms of mortality have consistently shown it to be superior to Child-Pugh scoring and logistic regression-based models; it is worth noting that MELD score is also derived using the logistic regression model. Due to the inherent ability of neural network-based systems in identifying complex nonlinear interactions, ANN-based models are expected to perform better than most linear models, such as regression-based models. More studies are needed on ANN-based models for prediction of mortality of patients with cirrhosis of liver and its value in prioritization of organ allocation for treatment of patients with cirrhosis of liver.

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Section: What Is An Artificial Neural Network?mentioning

confidence: 99%

Section: Review Of Studies On Ann For Prediction Of the Outcome Of Chmentioning

confidence: 99%

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Models for prediction of mortality from cirrhosis with special reference to artificial neural network: a critical review

Ghoshal

Das

2007

Hepatol Int

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“…While neural networks are by no means a novelty in automated diagnostic systems (SHARPE and CALEB, 1994;YANG et al, 1994;ACCORNERO and CAPOZZA, 1995;ARMONI, 1998), the diagnostic task presented is unusual because it entails topographic rather than semantic diagnosis. In conventional diagnostic systems, numerical outputs represent the likelihood that the various nosographic entities considered will be true in the observed subject.…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional mapping of brainstem functional lesions

Capozza

Iannetti

Mostarda

et al. 2000

Med. Biol. Eng. Comput.

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The human brainstem is a highly complex structure where even small lesions can give rise to a variety of symptoms and outward signs. Localising the area of dysfunction within the brainstem is often a difficult task. To make localisation easier, a neural net system has been developed which uses 72 clinical and neurophysiological data inputs to provide a display (using 5268 voxels) on a three-dimensional model of the human brainstem. The net was trained by means of a back-propagation algorithm, over a pool of 580 example cases. Assessed on 200 test cases, the net correctly localised 83.6% of the target voxels; furthermore the net correctly localised the lesions in 31 out of 37 patients. Because this computer-assisted method provides reliable and quantitative localisation of brainstem areas of dysfunction and can be used as a 3D interactive functional atlas, it is expected to prove useful as a diagnostic tool for assessing focal brainstem lesions.

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“…Artificial neural network analysis Several reviews on ANN have been published recently [8][9][10][11][12][13][14][15][16]. In order to design the ANN, we used the Neural Network Toolbox of the MATLAB 7 software.…”

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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Artificial neural networks within medical decision support systems

Cited by 15 publications

References 11 publications

Models for prediction of mortality from cirrhosis with special reference to artificial neural network: a critical review

Models for prediction of mortality from cirrhosis with special reference to artificial neural network: a critical review

Three-dimensional mapping of brainstem functional lesions

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

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