Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective.
Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective.
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DOI：
10.3233/jad-221261
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发表时间：
2023-03
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通讯作者：
Jay Shah;Md Mahfuzur Rahman Siddiquee;J. Krell-Roesch;Jeremy A. Syrjanen;W. Kremers;M. Vassilaki;E. Forz
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文献类型：
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作者：
Jay Shah;Md Mahfuzur Rahman Siddiquee;J. Krell-Roesch;Jeremy A. Syrjanen;W. Kremers;M. Vassilaki;E. Forz
人们对机器学习 (ML) 在阿尔茨海默病 (AD) 研究中的应用越来越感兴趣。然而，AD、轻度认知障碍 (MCI) 和其他相关痴呆症患者中常见的神经精神症状 (NPS) 尚未使用 ML 方法进行充分分析。为了描绘 ML 研究在 AD 和 NPS 研究中的前景和潜力，我们对现有 ML 方法和常见研究的 AD 生物标志物进行了全面的文献综述。我们使用与 NPS、AD 生物标志物、机器学习和认知相关的关键词进行 PubMed 搜索。在从搜索结果中排除一些不相关的研究后，我们在这篇综述中总共纳入了 38 篇文章，并通过滚雪球式搜索相关研究的参考书目纳入了 6 篇文章。我们发现，针对有或没有 AD 生物标志物的 NPS 的研究数量有限。相比之下，多种统计机器学习和深度学习方法已被用于利用众所周知的 AD 生物标志物构建预测诊断模型。这些主要包括多种成像生物标志物、认知评分和各种组学生物标志物。结合这些生物标志物或多模态数据集的深度学习方法通常优于单模态数据集。我们的结论是，机器学习可以用来理清 NPS 和 AD 生物标志物与认知之间的复杂关系。这可能有助于预测 MCI 或痴呆症的进展，并基于 NPS 开发更有针对性的早期干预方法。
There is a growing interest in the application of machine learning (ML) in Alzheimer's disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS.