Some Examples for Solving Clinical Problems Using Neural Networks

Serrano, Adela; Soria, Emilio; Camps, G.; Guerrero, José David Martín; Jiménez, N. V.

doi:10.1007/3-540-45723-2_41

Cited by 3 publications

(3 citation statements)

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“…In our experience the endoscopic outcome of treating VUR was similar to those in other series treating primary and secondary VUR [16]. Neural networks have been proposed as useful aids for decision-making in various medical fields [8,11,13,17,18] but they have not yet been applied to VUR. Numerous factors are considered to be related to the resolution of VUR treated endoscopically; some of them depend on the patient and some on the treatment [3,[5][6][7].…”

Section: Discussionsupporting

confidence: 74%

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The use of neural networks for predicting the result of endoscopic treatment for vesico‐ureteric reflux

et al. 2004

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the endoscopic findings, and the type of cystography used. In all, 174 cases were used as training samples for the ANN and 87 to validate it. We calculated the sensitivity, specificity, positive (PPV) and negative predictive values (NPV), and the success rate (%) of the system. RESULTSIn the training group the ANN gave a sensitivity of 86.4%, a specificity of 89.5%, a PPV of 76% and NPV of 94%, with a success rate of 88.6%. In the same training group logistic regression (LR) gave respective values of 68.2%, 58.8%, 39%, 82.7% and 61.4%. In the validation group the respective values for the ANN were 71.4%, 81.6%, 58.8%, 88.6% and 78.9%, and in the same validation group the LR gave 64.4%, 50%, 32.1%, 79.2% and 53.9%. The Wilcoxon test confirmed the independence of both methods ( P < 0.001). CONCLUSIONThe ANN is an effective tool for assisting the urologist in indicating and applying endoscopic treatments for VUR.

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Section: Discussionsupporting

confidence: 74%

Section: Discussionsupporting

confidence: 72%

The use of neural networks for predicting the result of endoscopic treatment for vesico‐ureteric reflux

et al. 2004

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“…2): En primer lugar, los datos son introducidos en cada una de las neuronas de la capa de entrada. A continuación [32], se calcula el valor del resto de neuronas (3). b: Sesgo de la capa anterior (peso que está conectado a una neurona adicional que siempre vale uno).…”

Section: Redes Neuronalesunclassified

Navigation of mobile robots using neural networks and genetic algorithms

Abad Perez,

Al-Hadithi,

Cadix Martin

2024

IEEE Latin Am. Trans.

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The navigation of robots has been a subject of widespread interest over the last few decades. In the previous years, traditional methods based on mathematical equations were used, and there has been an evolution towards the use of methods based on artificial intelligence. Two of which have been used in this work: neural networks and genetic algorithms. Neural networks are used as a machine learning model to teach the robot to move from any starting point to a goal, avoiding obstacles along the way. However, this model needs an algorithm to learn how to carry out this activity, which is what the genetic algorithm will be used for. Furthermore, this method of navigation will be compared with the traditional method based on potential fields, where it can be observed how this new method based on artificial intelligence improves and solves some typical problems of the old methods, such as the tendency to get stuck in local minima.

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Clinical Applications of Artificial Neural Networks in Pharmacokinetic Modeling

Kareem

Pathak

2016

Artificial Neural Network for Drug Design, Delivery and Disposition

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Some Examples for Solving Clinical Problems Using Neural Networks

Cited by 3 publications

References 3 publications

The use of neural networks for predicting the result of endoscopic treatment for vesico‐ureteric reflux

The use of neural networks for predicting the result of endoscopic treatment for vesico‐ureteric reflux

Navigation of mobile robots using neural networks and genetic algorithms

Clinical Applications of Artificial Neural Networks in Pharmacokinetic Modeling

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