Zheng Haoyang, Petrella Jeffrey R, Doraiswamy P Murali, Lin Guang, Hao Wenrui
School of Mechanical Engineering, Purdue University, West Lafayette, 47907, IN, USA.
Department of Radiology, Duke University Health System, Durham, 27710, NC, USA.
NPJ Digit Med. 2022 Sep 8;5(1):137. doi: 10.1038/s41746-022-00632-7.
With the explosive growth of biomarker data in Alzheimer's disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model's parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer's Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.
随着阿尔茨海默病（AD）临床试验中生物标志物数据的爆炸式增长，人们开发了许多数学模型来描述与疾病相关的生物标志物随时间的变化轨迹。虽然其中一些模型纯粹是经验性的，但其他模型是因果性的，基于AD病理生理学的各种假设构建，而这是一个复杂且尚未完全理解的研究领域。计算因果模型中最具挑战性的问题之一是使用纯粹的数据驱动方法来推导模型参数和数学模型本身，而不存在任何先验假设偏差。在本文中，我们开发了一种创新的数据驱动建模方法，以构建和参数化一个因果模型来描述AD生物标志物的变化轨迹。这种方法整合了因果模型学习、总体参数化、参数敏感性分析和个性化预测。通过将这种综合方法应用于一个大型多中心AD生物标志物数据库——阿尔茨海默病神经影像学计划，揭示了针对不同AD阶段的几种因果模型。此外，还为每个受试者校准了个性化模型，并对未来认知状态提供了准确预测。
