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Updated: Jul 18, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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基于高图隐性关系的多模式分类框架,用于与轻度认知障碍相关的末期脏病

Xidong Fu1, Chaofan Song1, Rupu Zhang1

  • 1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China.

Bioengineering (Basel, Switzerland)
|August 26, 2023
PubMed
概括

这项研究引入了一种结合动脉自旋标记 (ASL) 和功能磁共振成像 (fMRI) 的新方法,以识别轻度认知障碍末期病 (ESRDaMCI) 中的大脑网络变化. 该方法实现了88.67%的准确性,改进了现有方法.

关键词:
最终阶段的脏疾病.过度图形 (hypergraph) 是一个超图形.隐藏的关系适应的相似性学习.轻度的认知障碍 轻度的认知障碍多式联运分类框架 多式联运分类框架

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