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基于文本挖掘的精神障碍相关主要脑结构研究。

Research on major brain structures related to mental disorders based on text mining.

机构信息

School of Humanities and Social Sciences, Shanxi Medical University, Taiyuan, China.

School of Humanities and Social Sciences, Shanxi Medical University, Taiyuan, China.

出版信息

Psychiatry Res. 2020 Sep;291:113217. doi: 10.1016/j.psychres.2020.113217. Epub 2020 Jun 9.

Abstract

Mental disorders are recognized as leading causes of morbidity and disability worldwide, but the etiology of mental disorders is not completely clear, and it is generally believed that genetic and environmental factors are involved. A number of brain imaging researches showed that patients with mental disorders often had multiple brain structural abnormalities. Which brain structures are dominant in the pathogenesis of mental disorders, and whether there is a regular relationship between them need to be further studied. In this study, we used text mining technology to analyze the literatures related to mental disorders and brain structures in Pubmed database. Firstly, 61 high-frequency brain structures identified as the major brain structures. Then, from the results of system clustering, the major brain structures were divided into three clusters. Finally, 29 frequent itemsets and 36 strong association rules were obtained by association analysis. This study applied text mining technology to summarize and clarify the relationship between mental disorders and brain structures, providing possible direction and reference for future experimental studies.

摘要

精神障碍被公认为全球范围内导致发病和残疾的主要原因,但精神障碍的病因并不完全清楚,一般认为涉及遗传和环境因素。许多脑影像学研究表明,精神障碍患者常存在多种脑结构异常。在精神障碍的发病机制中,哪些脑结构占主导地位,以及它们之间是否存在有规律的关系,需要进一步研究。本研究采用文本挖掘技术,分析 Pubmed 数据库中与精神障碍和脑结构相关的文献。首先,确定了 61 个高频脑结构作为主要脑结构。然后,从系统聚类的结果来看,主要脑结构分为三个簇。最后,通过关联分析得到 29 个频繁项集和 36 个强关联规则。本研究应用文本挖掘技术对精神障碍与脑结构之间的关系进行了总结和阐明,为未来的实验研究提供了可能的方向和参考。

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