Albright Jack
The Nueva School, San Mateo, CA.
Alzheimers Dement (N Y). 2019 Sep 25;5:483-491. doi: 10.1016/j.trci.2019.07.001. eCollection 2019.
There is a 99.6% failure rate of clinical trials for drugs to treat Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily identified at early stages. This study investigated machine learning approaches to use clinical data to predict the progression of AD in future years.
Data from 1737 patients were processed using the "All-Pairs" technique, a novel methodology created for this study involving the comparison of all possible pairs of temporal data points for each patient. Machine learning models were trained on these processed data and evaluated using a separate testing data set (110 patients).
A neural network model was effective (mAUC = 0.866) at predicting the progression of AD, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment.
Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics.
治疗阿尔茨海默病的药物临床试验失败率达99.6%，这可能是因为阿尔茨海默病（AD）患者在早期阶段难以被轻易识别。本研究调查了利用机器学习方法，通过临床数据预测未来几年AD病情进展的情况。
采用“全配对”技术对1737例患者的数据进行处理，该技术是为本研究创建的一种新方法，涉及比较每个患者所有可能的时间数据点对。基于这些处理后的数据训练机器学习模型，并使用一个单独的测试数据集（110例患者）进行评估。
一个神经网络模型在预测AD病情进展方面很有效（平均曲线下面积=0.866），无论是对最初认知正常的患者还是患有轻度认知障碍的患者。
这样一个模型可用于识别处于AD早期阶段的患者，这些患者因此是AD治疗临床试验的理想人选。
