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使用概率主要成分分析的阿尔茨海默氏症疾病疾病进展建模

Martin Saint-Jalmes¹, Victor Fedyashov², Daniel Beck³

¹ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia.

|July 16, 2023

概括

这项研究引入了一种新方法,通过将生物标志物组合成单一评分来跟踪阿尔茨海默病 (AD) 的进展. 这一分数准确地反映了疾病的进展,并且对缺失的数据具有稳定性,有助于早期诊断和患者监测.

科学领域:

神经科学是一个神经科学.
生物统计学生物统计学
医疗成像医学成像

背景情况:

对阿尔茨海默病 (AD) 的生物重新定义需要先进的统计模型来使用生物标志物跟踪疾病进展.
疾病进展模型 (DPM) 对于了解AD生物标志物的时间动态至关重要,但由于疾病异质性和缺少数据,估计患者特定的时间转移仍然具有挑战性.

研究的目的:

通过整合多个生物标志物,开发一种可靠的方法来估计个体阿尔茨海默病的进展.
创建一个统一的进展分数,能够抵御缺失的数据,并可以作为队列分析的伪时间尺度.

主要方法:

利用主要成分分析的概率公式,将多个AD生物标志物结合到一个潜在变量中.
将该方法应用于来自阿尔茨海默病神经成像计划 (ADNI) 的横截面数据,并评估其在缺少数据的情况下的性能.

主要成果:

推导的进展得分与从基线的纵向混合效应模型中共同估计的时间转移密切一致.
估计的得分证明了对缺少数据或不可用的生物标志物的稳定性,这对于现实世界的临床数据集至关重要.
该得分可以用纵向数据建模为个人随时间推移的进展得分,改进长期估计.

结论:

拟议的方法提供了一种可靠的,数据驱动的方法,使用可观测的生物标志物量化个体阿尔茨海默病的进展.
这种统一的得分可能会在队列研究中取代年龄作为时间尺度,减轻患者异质性并揭示一般生物标志物演变趋势.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.疾病进展建模的模型.潜在的疾病时间机器学习是机器学习.主要组件分析的主要组件分析.

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