Alzheimer's disease risk assessment using large-scale machine learning methods.
Alzheimer's disease risk assessment using large-scale machine learning methods.
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DOI：
10.1371/journal.pone.0077949
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Casanova R;Hsu FC;Sink KM;Rapp SR;Williamson JD;Resnick SM;Espeland MA;Alzheimer's Disease Neuroimaging Initiative
这项工作的目标是引入新的指标来评估阿尔茨海默病 (AD) 的风险，我们称之为 AD 模式相似性 (AD-PS) 评分。这些指标是通过大规模正则逻辑回归建模的条件概率。使用阿尔茨海默病神经影像倡议 (ADNI) 研究的数据在不同情况下测试了从结构 MRI 和认知测试数据得出的 AD-PS 分数。分数是根据认知状态、年龄和功能状态分层的参与者组进行计算的。 Cox 比例风险回归用于评估与从轻度认知障碍到 AD 转换时间分布的关联。使用来自不同类型脑组织的数据开发的分类器的性能在认知状态组中被系统地表征。我们还探讨了通过组合使用不同类型数据开发的分类器的输出而生成的解剖和认知解剖综合分数的性能。此外，我们还提供了相对于该领域使用的其他指标的 AD-PS 评分表现，包括早期 AD 识别异常空间模式 (SPARE-AD) 指数和所检查变量的海马总体积。
The goal of this work is to introduce new metrics to assess risk of Alzheimer's disease (AD) which we call AD Pattern Similarity (AD-PS) scores. These metrics are the conditional probabilities modeled by large-scale regularized logistic regression. The AD-PS scores derived from structural MRI and cognitive test data were tested across different situations using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The scores were computed across groups of participants stratified by cognitive status, age and functional status. Cox proportional hazards regression was used to evaluate associations with the distribution of conversion times from mild cognitive impairment to AD. The performances of classifiers developed using data from different types of brain tissue were systematically characterized across cognitive status groups. We also explored the performance of anatomical and cognitive-anatomical composite scores generated by combining the outputs of classifiers developed using different types of data. In addition, we provide the AD-PS scores performance relative to other metrics used in the field including the Spatial Pattern of Abnormalities for Recognition of Early AD (SPARE-AD) index and total hippocampal volume for the variables examined.
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