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当代阿耳茨海默病研究

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ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

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Letter Article

使用无监督机器学习和高斯混合建模稳健发现轻度认知障碍亚型及其阿尔茨海默病转化风险

卷 18, 期 7, 2021

发表于: 31 August, 2021

页: [595 - 606] 页: 12

弟呕挨: 10.2174/1567205018666210831145825

价格: $65

摘要

背景：阿尔茨海默病 (AD) 是一种不可逆的、进行性的脑部疾病，会缓慢破坏记忆力和思维能力。在早期阶段正确预测阿尔茨海默病诊断的能力可以帮助医生对治疗计划做出更明智的临床决策。 目标：本研究旨在确定无监督发现轻度认知障碍 (MCI) 受试者的潜在类别是否有助于发现不同的前驱 AD 阶段和/或具有低 MCI 到 AD 转换风险的受试者。 方法：总共 18 个与 MCI 到 AD 转换过程相关的特征导致 681 名早期 MCI 受试者的识别。受试者被分为训练 (70%) 和验证 (30%) 组。使用共识聚类分析训练集中的受试者，并使用高斯混合模型 (GMM) 来描述潜在类别。发现的 GMM 预测了验证集的潜在类别。最后，为每个发现的类别计算描述性统计数据、转换率和优势比 (OR)。 结果：通过共识聚类，我们在 MCI 科目中发现了三个不同的聚类。这三个集群与低风险（OR = 0.12, 95%CI = 0.04 to 0.3|）、中风险（OR = 1.33, 95%CI = 0.75 to 2.37）和高风险（OR = 3.02,从 MCI 转换为 AD 的 95%CI = 1.64 到 5.57），高风险和低风险组高度对比。因此，前驱 AD 受试者仅出现在两个集群中。 结论：我们通过共识聚类成功地发现了 MCI 受试者中具有不同 MCI 到 AD 转换风险的三个不同的潜在类别。发现的两个类别可能代表阿尔茨海默病的两种不同的前驱表现。

关键词: 阿尔茨海默病、轻度认知障碍、潜在类别分析、共识聚类、高斯混合模型、颅内容积。

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