Buscema Massimo
Semeion Research Center for Science and Communication, Rome, Italy.
Subst Use Misuse. 2002 Jun-Aug;37(8-10):1093-148. doi: 10.1081/ja-120004171.
This article is designed to acquaint professionals working in the field of substance use intervention with a range of artificial intelligence nonlinear, powerful tools, artificial neural networks, concepts, and paradigms. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data as well as our studying and understanding the many people, processes, and phenomena which comprise substance use and its intervention. The latter represent complex, dynamic, multidimensional phenomena which are unpredictable and uncontrollable in the traditional "cause and effect" sense. As such they are likely to be nonlinear in their very essence. Using linear-based paradigms for planned intervention with nonlinear phenomena brooks the all-too-common possibility of using inappropriate intervention paradigms and/or drawing misleading conclusions about what is and/or has happened.
本文旨在让从事物质使用干预领域工作的专业人员了解一系列人工智能非线性强大工具、人工神经网络、概念和范式。当适当选择和使用时,人工神经网络家族能够最大限度地利用现有数据,并有助于我们研究和理解构成物质使用及其干预的众多人员、过程和现象。后者代表着复杂、动态、多维度的现象,在传统的“因果”意义上是不可预测和不可控制的。因此,它们本质上可能是非线性的。使用基于线性的范式对非线性现象进行有计划的干预,很可能会出现使用不适当的干预范式和/或对正在发生和/或已经发生的事情得出误导性结论这种非常常见的情况。