了解痴呆症研究和临床实践中的机器学习应用:生物医学科学家和临床医生的评论
Yihan Wang1,2, Shu Liu1,2,3, Alanna G Spiteri1
1The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia.
Alzheimer's research & therapy
|July 31, 2024
概括
机器学习 (ML) 通过分析复杂的数据集来早期诊断和预测疾病进展,为痴呆症研究提供了强大的工具. 本综述探讨了ML的应用,挑战及其改变痴呆症护理的潜力.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 生物医学数据科学 生物医学数据科学
背景情况:
- 纵向痴呆症数据集集整合了各种数据类型 (人口统计,神经成像,生物标志物,omics).
- 机器学习 (ML) 在分析复杂的研究问题上的多模式,高维数据方面表现出色.
研究的目的:
- 审查ML在痴呆症研究中的当前和潜在应用.
- 为生物医学科学家和临床医生提供ML的非技术概述.
- 确定临床ML实施的技术障碍和挑战.
主要方法:
- 审查关于ML在痴呆症中的应用现有的文献.
- 分析ML工作流程和数据集要求.
- 将ML与传统统计方法进行比较.
主要成果:
- ML促进痴呆和轻度认知障碍的早期和差异诊断.
- ML有助于预测神经退行性疾病的发病和进展.
- 确定的技术障碍包括数据集成,可解释性和临床验证.
结论:
- 机器学习对推进痴呆症研究和临床实践具有重大前景.
- 解决技术挑战对于在医疗保健中成功实施ML至关重要.
- 这一审查提高了对更广泛的科学受众的ML概念的可访问性.
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