Feed-forward neural network model for hunger and satiety related VAS score prediction.

Q1 Mathematics
Shaji Krishnan, Henk F J Hendriks, Merete L Hartvigsen, Albert A de Graaf
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引用次数: 8

Abstract

Background: An artificial neural network approach was chosen to model the outcome of the complex signaling pathways in the gastro-intestinal tract and other peripheral organs that eventually produce the satiety feeling in the brain upon feeding.

Methods: A multilayer feed-forward neural network was trained with sets of experimental data relating concentration-time courses of plasma satiety hormones to Visual Analog Scales (VAS) scores. The network successfully predicted VAS responses from sets of satiety hormone data obtained in experiments using different food compositions.

Results: The correlation coefficients for the predicted VAS responses for test sets having i) a full set of three satiety hormones, ii) a set of only two satiety hormones, and iii) a set of only one satiety hormone were 0.96, 0.96, and 0.89, respectively. The predicted VAS responses discriminated the satiety effects of high satiating food types from less satiating food types both in orally fed and ileal infused forms.

Conclusions: From this application of artificial neural networks, one may conclude that neural network models are very suitable to describe situations where behavior is complex and incompletely understood. However, training data sets that fit the experimental conditions need to be available.

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前馈神经网络模型用于饥饿和饱腹感相关VAS评分预测。
背景:采用人工神经网络的方法来模拟胃肠道和其他外周器官中复杂的信号通路的结果,这些信号通路最终在进食时在大脑中产生饱腹感。方法:采用血浆饱腹激素浓度-时间过程与视觉模拟量表(VAS)评分相关的实验数据对多层前馈神经网络进行训练。该网络成功地从实验中获得的不同食物成分的饱腹激素数据集预测VAS反应。结果:i)全套三种饱腹感激素、ii)仅两种饱腹感激素和iii)仅一种饱腹感激素的测试集预测VAS反应的相关系数分别为0.96、0.96和0.89。预测的VAS反应区分了高饱腹感食物类型和低饱腹感食物类型在口服和回肠灌注形式下的饱腹感效果。结论:从人工神经网络的应用中,我们可以得出结论,神经网络模型非常适合描述行为复杂和不完全理解的情况。然而,需要有符合实验条件的训练数据集。
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来源期刊
Theoretical Biology and Medical Modelling
Theoretical Biology and Medical Modelling MATHEMATICAL & COMPUTATIONAL BIOLOGY-
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Theoretical Biology and Medical Modelling is an open access peer-reviewed journal adopting a broad definition of "biology" and focusing on theoretical ideas and models associated with developments in biology and medicine. Mathematicians, biologists and clinicians of various specialisms, philosophers and historians of science are all contributing to the emergence of novel concepts in an age of systems biology, bioinformatics and computer modelling. This is the field in which Theoretical Biology and Medical Modelling operates. We welcome submissions that are technically sound and offering either improved understanding in biology and medicine or progress in theory or method.
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