We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine techniques of modern machine learning with an interpretable interaction network, where health variables are coupled by explicit pair-wise interactions within a stochastic dynamical system. Our model is scalable to large longitudinal data sets, is predictive of individual high-dimensional health trajectories and survival from baseline health states, and infers an interpretable network of directed interactions between the health variables. The network identifies plausible physiological connections between health variables and clusters of strongly connected heath variables. We use English Longitudinal Study of Aging (ELSA) data to train our model and show that it performs better than dedicated linear models for health outcomes and survival. Our model can also be used to generate synthetic individuals that age realistically, to impute missing data, and to simulate future aging outcomes given arbitrary initial health states.
翻译:我们建立了个人健康和生存老化轨迹的计算模型,其中包含物理、功能和生物变量,并以人口、生活方式和医疗背景信息为条件。我们把现代机器学习技术与可解释的互动网络结合起来,在这个网络中,健康变量与透视动态系统中的明显双向互动相结合。我们的模型可伸缩到大型纵向数据集,预测来自基线卫生状况的个人高维健康轨迹和生存,并推断出健康变量之间可解释的定向互动网络。这个网络确定了健康变量和紧密相连的热量变量组合之间的合理生理联系。我们用英国长视研究(ELSA)数据来培训我们的模型,并表明它比专门的线性健康结果和生存模型要好。我们的模型还可以用来产生现实的合成个体,污染缺失的数据,以及根据任意的初始健康状况模拟未来老龄化结果。