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一种复杂网络方法的临床科学应用。

A complex network approach to clinical science.

机构信息

Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts.

出版信息

Eur J Clin Invest. 2018 Aug;48(8):e12986. doi: 10.1111/eci.12986. Epub 2018 Jul 5.

Abstract

Contemporary classification systems assume that psychiatric disorders are expressions of latent disease entities. However, some critics point to the comorbidity problem and other issues that question the validity of the latent disease model. An alternative to this traditional view is the complex network approach. This approach assumes that disorders exist as systems of inter-connected elements, without requiring that the elements are expressions of latent disease entities. Depending on the structure of the network, change can occur abruptly once the network reaches a tipping point. A dynamic complex network approach could be used to develop a functional analytic case conceptualization that may predict treatment change, relapse and recovery, thereby linking nosology and treatment. In conclusion, the complex network perspective offers an alternative and less restrictive approach to the latent disease model, while offering exciting new directions for future research in psychiatry.

摘要

当代分类系统假设精神障碍是潜在疾病实体的表现。然而,一些批评者指出了共病问题和其他质疑潜在疾病模型有效性的问题。这种传统观点的替代方法是复杂网络方法。该方法假设障碍作为相互关联的元素系统存在,而不需要这些元素是潜在疾病实体的表现。根据网络的结构,一旦网络达到临界点,变化就可能突然发生。动态复杂网络方法可用于开发功能分析案例概念化,从而可以预测治疗变化、复发和恢复,从而将分类学和治疗联系起来。总之,复杂网络视角为潜在疾病模型提供了一种替代的、限制较少的方法,同时为精神病学的未来研究提供了令人兴奋的新方向。

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