阿尔茨海默病的多因素性质:对多种决定因素和多模式机器学习的重要作用的回顾

Hesameddin Mostaghimi , Daniel A. Cohen , Hamid. R. Okhravi , Bahar Niknejad , Michel A. Audette
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引用次数: 0

摘要

阿尔茨海默病(AD)是最常见的痴呆症形式,由各种决定因素复杂的相互作用引起,包括神经和认知障碍、分子和遗传标记、全身合并症和生活方式相关因素。虽然传统的研究往往集中在个别或狭窄的决定因素,但最近的进展强调了统一检查这些不同因素的必要性。此外,医疗保健领域异构多模式数据的快速增长需要复杂的分析框架。在这篇综述中,我们首先总结了有关AD危险因素和机制的广泛证据,然后讨论了多模态机器学习(ML)技术在整合复杂数据集方面的必要性和潜力,这可能最终导致针对该疾病的个性化治疗策略。这篇叙述性综述定性地综合了2010年至2024年间发表的250项同行评议研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The multifactorial nature of Alzheimer’s disease: A review of diverse determinants and the essential role of multimodal machine learning
Alzheimer’s disease (AD), the most prevalent form of dementia, arises from a complex interplay of determinants, including neurological and cognitive impairments, molecular and genetic markers, systemic comorbidities, and lifestyle-related factors. While traditional research has often focused on individual or narrow sets of determinants, recent advancements highlight the necessity of examining these diverse contributors in unison. In addition, the rapid growth of heterogeneous multimodal data in healthcare necessitates sophisticated analytical frameworks. In this review, we first summarize the evidence on the broad spectrum of AD risk factors and mechanisms, and then discuss the necessity and potential of multimodal machine learning (ML) techniques in integrating complex datasets, which could ultimately lead to personalized therapeutic strategies for this disease. This narrative review qualitatively synthesizes 250 peer-reviewed studies published between 2010 and 2024.
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