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用预测模型优化对亨廷顿病内层干预的查,使用预测模型.

Matthew J Barrett1, Ahmed Negida1, Nitai Mukhopadhyay2

  • 1Department of Neurology, Virginia Commonwealth University, Richmond, Virginia, USA.

Movement disorders : official journal of the Movement Disorder Society
|March 11, 2024
PubMed
概括
此摘要是机器生成的。

使用临床数据的预测模型可以识别具有足够条纹体积的亨廷顿病 (HD) 患者进行临床试验. 这种方法可以加快HD和其他需要体积评估的神经退行性疾病的入学率.

关键词:
亨廷顿病就是亨廷顿病.紧紧跟在后面的时间.机器学习是机器学习.磁共振成像技术的使用条纹体的条纹体.

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科学领域:

  • 神经科学是一个神经科学.
  • 医疗成像医学成像
  • 机器学习 机器学习

背景情况:

  • 亨廷顿病 (HD) 治疗药物的内输送需要足够的尾状和状体积.
  • 目前的体积核磁共振 (MRI) 并非临床实践中的标准,并且缺乏大HD队列的数据.

研究的目的:

  • 开发和验证预测模型来分类超出状腺体体积值的HD患者进行状腺体内治疗.
  • 评估在HD临床试验中使用机器学习模型进行预先查的可行性.

主要方法:

  • 合并了来自3个HD队列 (IMAGE-HD,PREDICT-HD,TRACK-HD/TRACK-ON) 的1374人的数据.
  • 利用BORUTA算法从临床数据中识别出10个关键预测变量.
  • 开发并比较随机森林和物流回归模型来预测条形状体积.

主要成果:

  • 随机森林模型实现了曲线下面积 (AUC) 的83%.
  • 使用年龄,CAG重复大小和符号数字模式测试的后勤回归模型,产生了85.1%的AUC.
  • 概率截止值为0.8,导致低虚假阳性 (5.4%) 和高虚假阴性 (66.7%) 率.

结论:

  • 机器学习模型 (随机森林,后勤回归) 使用可访问的临床数据有效地识别具有足够条状体积的个体.
  • 这些模型可以简化对HD和其他神经退行性疾病的临床试验招生,使用基于体积的纳入标准.
  • 在预先查中的实施可以加速对需要体积评估的疾病的研究和治疗开发.