Clinical Applications of Artificial Neural Networks in Pharmacokinetic Modeling

Kareem, Syeda Saba; Pathak, Yashwant

doi:10.1016/b978-0-12-801559-9.00020-x

Cited by 7 publications

(6 citation statements)

References 49 publications

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“…An artificial neural network is a computational model designed to mimic the biological nervous system [53]. Another possible definition would be that an ANN is a nonlinear statistical technique that can be used to model the intricate relationship between data (called input and output data) [12].…”

Section: Artificial Neural Network Modelsmentioning

confidence: 99%

Global Solar Irradiation Modelling and Prediction Using Machine Learning Models for Their Potential Use in Renewable Energy Applications

Puga-Gil,

Astray,

Barreiro

et al. 2022

Mathematics

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Global solar irradiation is an important variable that can be used to determine the suitability of an area to install solar systems; nevertheless, due to the limitations of requiring measurement stations around the entire world, it can be correlated with different meteorological parameters. To confront this issue, different locations in Rias Baixas (Autonomous Community of Galicia, Spain) and combinations of parameters (month and average temperature, among others) were used to develop various machine learning models (random forest -RF-, support vector machine -SVM- and artificial neural network -ANN-). These three approaches were used to model and predict (one month ahead) monthly global solar irradiation using the data from six measurement stations. Afterwards, these models were applied to seven different measurement stations to check if the knowledge acquired could be extrapolated to other locations. In general, the ANN models offered the best results for the development and testing phases of the model, as well as for the phase of knowledge extrapolation to other locations. In this sense, the selected ANNs obtained a mean absolute percentage error (MAPE) value between 3.9 and 13.8% for the model development and an overall MAPE between 4.1 and 12.5% for the other seven locations. ANNs can be a capable tool for modelling and predicting monthly global solar irradiation in areas where data are available and for extrapolating this knowledge to nearby areas.

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Section: Artificial Neural Network Modelsmentioning

confidence: 99%

Global Solar Irradiation Modelling and Prediction Using Machine Learning Models for Their Potential Use in Renewable Energy Applications

Puga-Gil,

Astray,

Barreiro

et al. 2022

Mathematics

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“…Éstas se han utilizado ampliamente en aplicaciones enfocadas a modelado de sistemas biológicos complejos, debido a su capacidad de reconocer patrones y relaciones de tipo no lineal, procesar variables complejas y modelar funciones de predicción [7,8] . Estas redes están formadas por varias unidades elementales llamadas neuronas, interconectadas entre sí, donde cada una tiene un peso.…”

Section: Introductionunclassified

“…son un método que ha demostrado un buen funcionamiento en el área del modelado farmacocinético y farmacodinámico debido a su capacidad de simular sistemas no lineales y procesar variables fisiológicas dependientes e independientes [7,9] . Éstas redes han sido ampliamente utilizadas en sistemas complejos que requieren predicción, sistema de soporte de decisiones, pacientes tratados con digoxina, entre otros [10][11][12][13] .…”

Section: Introductionunclassified

Predicting the Cardioactivity of Tivela stultorum clam with Digoxin Using Artificial Neural Networks

Flores

Cervantes

Abaroa

et al. 2017

Rev. Mex. Ing. Biomed.

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RESUMENLas redes neuronales artificiales (RNA) son un método computacional extensamente utilizado para resolver problemas complejos y realizar predicciones en sistemas de relación no lineal. En este trabajo se utilizaron RNA para predecir la respuesta fisiológica obtenida al adicionar una concentración específica de digoxina a corazones de Tivela stultorum, un organismo modelo para probar fármacos cardíacos que se pretenden utilizar en humanos. Las entradas de la RNA fueron el peso, volumen, largo y ancho del corazón, la concentración de digoxina, el volumen utilizado para la dilución de digoxina, el máximo y mínimo de contracción, tiempo de llenado, y frecuencia cardíaca antes de adicionar la digoxina, las salidas fueron el máximo y mínimo de contracción, tiempo de llenado y frecuencia cardíaca esperados después de agregar digoxina al corazón. Las RNA se entrenaron, validaron y probaron con los resultados de experimentos in vivo. Para elegir la red óptima se utilizó el valor más pequeño del error medio cuadrado. Se obtuvo una correlación alta entre los valores predichos y calculados, excepto en el caso del tiempo de llenado. Se lograron obtener predicciones acertadas de la cardioactividad de la almeja T. stultorum cuando se les agrega una concentración específica de digoxina haciendo uso de RNA; esto con el fin de utilizarse como una herramienta para facilitar las pruebas en el laboratorio de los efectos de la digoxina.

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“…Las redes neuronales artificiales (RNA) son un método computacional cuyo diseño y funcionamiento está basado en el comportamiento de las neuronas biológicas del cerebro humano. Éstas se han utilizado ampliamente en aplicaciones enfocadas a modelado de sistemas biológicos complejos, debido a su capacidad de reconocer patrones y relaciones de tipo no lineal, procesar variables complejas y modelar funciones de predicción [7,8] . Estas redes están formadas por varias unidades elementales llamadas neuronas, interconectadas entre sí, donde cada una tiene un peso.…”

Section: Introductionunclassified

“…Las RNA de topología perceptrón multicapa (PMC) son un método que ha demostrado un buen funcionamiento en el área del modelado farmacocinético y farmacodinámico debido a su capacidad de simular sistemas no lineales y procesar variables fisiológicas dependientes e independientes [7,9] . Éstas redes han sido ampliamente utilizadas en sistemas complejos que requieren predicción, sistema de soporte de decisiones, pacientes tratados con digoxina, entre otros [10][11][12][13] .…”

Section: Introductionunclassified

Predicting the Cardioactivity of Tivela stultorum clam with Digoxin Using Artificial Neural Networks

Flores¹

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Artificial neural networks (ANN) are a computational method that has been widely used to solve complex problems and carry out predictions on nonlinear systems. Multilayer perceptron artificial neural networks were used to predict the physiological response that would be obtained by adding a specific concentration of digoxin to Tivela stultorum hearts, this organism is a model for testing cardiac drugs that pretends to be used in humans. The MLPANN inputs were weight, volume, length, and width of the heart, digoxin concentration and volume used for diluting digoxin, and maximum contraction, minimum contraction, filling time, and heart rate before adding digoxin, and the outputs were the maximum contraction, minimum contraction, filling time, and heart rate that would be obtained after adding digoxin to the heart. ANNs were trained, validated, and tested with the results obtained from the in vivo experiments. To choose the optimal network, the smallest square mean error value was used. Perceptrons obtained a high performance and correlation between predicted and calculated values, except in the case of the filling time output. Accurate predictions of the T. stultorum clams cardioactivity were obtained when a specific concentration of digoxin was added using ANNs with one hidden layer; this could be useful as a tool to facilitate laboratory experiments to test digoxin effects.

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Clinical Applications of Artificial Neural Networks in Pharmacokinetic Modeling

Cited by 7 publications

References 49 publications

Global Solar Irradiation Modelling and Prediction Using Machine Learning Models for Their Potential Use in Renewable Energy Applications

Global Solar Irradiation Modelling and Prediction Using Machine Learning Models for Their Potential Use in Renewable Energy Applications

Predicting the Cardioactivity of Tivela stultorum clam with Digoxin Using Artificial Neural Networks

Predicting the Cardioactivity of Tivela stultorum clam with Digoxin Using Artificial Neural Networks

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