首页|Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervise d Machine Learning analysis
Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervise d Machine Learning analysis
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: “Background: The demand for fresh strategies to analyze intricate multidimension al data in neuroscience is increasingly evident. One of the most complex events during our neurodevelopment is adolescence, where our nervous system suffers con stant changes, not only in neuroanatomical traits, but also in neurophysiologica l components. One of the most impactful factors we deal with during this time is our environment, especially when encountering external factors such as social b ehaviors or substance consumption. Binge Drinking (BD) has emerged as an extende d pattern of alcohol consumption in teenagers, not only affecting their future l ifestyle, but changing their neurodevelopment. Recent studies have changed their scope into finding predisposition factors that may lead adolescents into this kind of patterns of consumption. Methods: In this article, using unsupervised mac hine learning (UML) algorithms, we analyze the relationship between electrophysi ological activity of healthy teenagers and the levels of consumption they had tw o years later.
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