利用深度学习推断抗高血压药对阿尔茨海默病的个性化治疗效果

Pulakesh Upadhyaya, Yaobin Ling, Luyao Chen, Yejin Kim, Xiaoqian Jiang
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摘要

阿尔茨海默病(AD)是美国人,尤其是老年人的主要死因之一。最近的研究表明,高血压与老年患者认知能力下降有关,而认知能力下降又会导致死亡率和发病率上升。有多项研究探讨了降压药对减少认知功能衰退的作用,但结果并不确定。然而,这些研究大多假设所有患者的治疗效果相似,因此只考虑了降压药物的平均治疗效果。在本文中,我们假设降压药对注意力缺失症发病的影响取决于患者的特征。我们开发了一种名为 LASSO-Dragonnet 的深度学习方法来估计每位患者的个性化治疗效果。我们考虑了六种抗高血压药物,六个模型中的每一个都将其中一种药物作为治疗药物,其余药物作为对照药物。我们的研究表明,虽然许多降压药平均而言对延缓AD发病有积极影响,但这种影响因人而异,取决于每个人的不同特征。我们还分析了各种协变量在这种估算中的重要性。我们的结果表明,使用深度学习方法可以准确估计出每位患者的个体化治疗效果,并且可以确定各种协变量的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inferring Personalized Treatment Effect of Antihypertensives on Alzheimer's Disease Using Deep Learning.

Alzheimer's disease (AD) is one of the leading causes of death in the United States, especially among the elderly. Recent studies have shown how hypertension is related to cognitive decline in elderly patients, which in turn leads to increased mortality as well as morbidity. There have been various studies that have looked at the effect of antihypertensive drugs in reducing cognitive decline, and their results have proved inconclusive. However, most of these studies assume the treatment effect is similar for all patients, thus considering only the average treatment effects of antihypertensive drugs. In this paper, we assume that the effect of antihypertensives on the onset of AD depends on patient characteristics. We develop a deep learning method called LASSO-Dragonnet to estimate the individualized treatment effects of each patient. We considered six antihypertensive drugs, and each of the six models considered one of the drugs as the treatment and the remaining as control. Our studies showed that although many antihypertensives have a positive impact in delaying AD onset on average, the impact varies from individual to individual, depending on their various characteristics. We also analyzed the importance of various covariates in such an estimation. Our results showed that the individualized treatment effects of each patient could be estimated accurately using a deep learning method, and that the importance of various covariates could be determined.

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