痴呆症的人工智能-应用模型和数字健康
Donald M Lyall1, Andrey Kormilitzin2, Claire Lancaster3
1School of Health and Wellbeing, College of Medical and Veterinary Sciences, University of Glasgow, Glasgow, UK.
概括
应用建模和数字健康技术在改善痴呆风险预测和诊断方面表现有前途. 这些先进的方法,使用多omics数据,提供比传统得分更大的预测能力.
科学领域:
- 计算神经科学是一种计算神经科学.
- 老年医学 老年医学
- 生物统计学 生物统计学
背景情况:
- 应用模型和数字健康技术对于痴呆风险预测,诊断和预后越来越重要.
- 具有多omics数据的深度表型定型队伍正在变得可用,为研究提供了新的机会.
研究的目的:
- 综合了解痴呆症应用模型和数字健康技术的理解.
- 审查它们在风险预测,诊断歧视,预后和进展方面的应用.
主要方法:
- 这是一个叙事评论.
- 它综合了人们对痴呆症研究中的应用模型和数字健康技术的当前理解.
主要成果:
- 与标准的临床风险得分相比,机器学习模型显示出对痴呆症的优越预测能力.
- 这些模型可以通过识别关键预测因素来简化复杂的数据集.
结论:
- 未来的进步将受益于改进的数据共享,多组和电子健康记录的整合,以及远程数据测量.
- 焦点正在转向预测二次结果,如治疗反应和身体健康,以及初级痴呆症预测.
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