A Complex Network Perspective on Clinical Science.

A Complex Network Perspective on Clinical Science.
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DOI:
10.1177/1745691616639283
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发表时间:
2016-09
期刊:
Perspectives on psychological science : a journal of the Association for Psychological Science
影响因子:
--
通讯作者:
McNally RJ
McNally RJ
中科院分区:
其他
文献类型:
--
作者:
Hofmann SG;Curtiss J;McNally RJ

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当代精神障碍分类系统假定异常行为是潜在疾病实体的表现。一种替代潜伏性疾病模型的方法是复杂网络方法。复杂网络方法不假设症状是由潜在的疾病实体引起的,而是认为疾病是由网络中相互关联的元素组成的系统。这种方法也为理解治疗改变提供了一个框架。根据网络的结构,一旦网络达到一个临界阈值(临界点),变化就会突然发生。当网络接近临界点时,同质和高度连接的网络通常从局部扰动中恢复得更慢,从而有可能预测治疗变化、复发和恢复。在本文中,我们讨论了复杂网络方法作为潜在疾病模型的替代方案,并讨论了其对分类、治疗、复发和恢复的影响。
Contemporary classification systems for mental disorders assume that abnormal behaviors are expressions of latent disease entities. An alternative to the latent disease model is the complex network approach. Instead of assuming that symptoms arise from an underlying disease entity, the complex network approach holds that disorders exist as systems of interrelated elements of a network. This approach also provides a framework for the understanding of therapeutic change. Depending on the structure of the network, change can occur abruptly once the network reaches a critical threshold (the tipping point). Homogeneous and highly connected networks often recover more slowly from local perturbations when the network approaches the tipping point, allowing for the possibility to predict treatment change, relapse, and recovery. In this article we discuss the complex network approach as an alternative to the latent disease model, and we discuss its implications for classification, therapy, relapse, and recovery.
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