在阿尔茨海默病和痴呆症中发现生物标志物的人工智能
Laura M Winchester1, Eric L Harshfield2, Liu Shi3
1Department of Psychiatry, Oxford University, Oxford, UK.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|September 1, 2023
概括
人工智能 (AI) 和机器学习可以加速复杂数据集中的生物标志物发现. 挑战包括数据的多样性和复杂性,但人工智能对准确,公正的临床生物标志物具有前景.
科学领域:
- 生物标志物发现发现
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 大型多式联络群体和高通量技术增加了新生物标志物发现的潜力.
- 人工智能 (AI) 和机器学习 (ML) 是用于检测复杂数据集中的生物标志物的新兴工具.
研究的目的:
- 讨论人工智能在生物标志物发现中的实例性应用.
- 评估人工智能的当前应用和局限性,以识别新的生物标志物.
主要方法:
- 在生物标志物发现中对示例AI和ML应用的审查.
- 评估人工智能驱动的生物标志物研究当前的挑战和局限性.
主要成果:
- 人工智能和机器学习显示出加速生物标志物发现的潜力,特别是对于像痴呆症这样的疾病.
- 关键的挑战包括数据集的多样性,相互作用的复杂性,生物标志物的侵入性/成本以及报告标准.
结论:
- 克服挑战需要多样化的数据,先进的人工智能,非侵入性生物标志物验证和遵守报告准则.
- 利用人工智能和协作的多式联网数据可以产生准确,可概括,公正和临床上可接受的生物标志物.
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