在一般人群中使用临床可访问的变量预测痴呆症:使用机器学习的概念验证研究. 根据AGES-雷克雅未克的研究
Emma L Twait1,2,3,4, Constanza L Andaur Navarro1, Vilmunur Gudnason5,6
1Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
BMC medical informatics and decision making
|August 28, 2023
概括
机器学习模型的性能与痴呆症预测的传统方法相美. 然而,当排除MRI数据时,弹性净Cox回归提供了轻微的改善,突出了临床使用的潜力.
科学领域:
- 神经学 神经学
- 生物统计学 生物统计学
- 人工智能的人工智能
背景情况:
- 早期识别痴呆症对于危险人群及时干预至关重要.
- 外部验证研究表明需要更新痴呆症预后模型.
- 机器学习 (ML) 显示了增强痴呆症预测准确性的前景.
研究的目的:
- 将ML算法的预测性能与所有原因痴呆症的传统统计方法 (逻辑和考克斯回归) 进行比较.
- 评估仅使用临床可访问的预测指标的可行性和影响,不包括MRI数据.
主要方法:
- 利用了AGES-雷克雅未克研究中的4793名参与者的数据 (平均年龄76岁).
- 将ML算法 (弹性网,随机森林,SVM,弹性网Cox) 与后勤和Cox回归进行比较.
- 评估了所有变量,选定的特征和临床可访问的预测因素 (不包括MRI) 的模型.
主要成果:
- 19%的参与者在12年内患上了痴呆症.
- 在所有模型中,ML算法的性能相当于物流回归.
- 弹性净考克斯回归在临床上可访问的模型 (c=0.78) 与传统的考克斯回归 (c=0.75) 相比,显示出轻微的性能增加.
结论:
- 监督的ML证明了增加的益处,主要是使用生存分析技术.
- 排除MRI标记物并没有显著损害模型性能.
- ML模型,包括名ograms,显示出临床实践的潜力,但需要外部验证.
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