SCH: Tackling Progressive Disease - Learning from Longitudinal Observational Clinical Data in the Presence of Noise and Confounding

迫切需要提高我们对患有阿尔茨海默病（AD）或有发展风险的患者进行分层和治疗的能力。为此，像阿尔茨海默病神经影像学倡议（ADNI）这样的努力旨在收集各种生物标志物的数据，并招募了数百名参与者进行纵向跟踪。虽然这些研究是重要和必要的，但有证据表明，AD的发展早在症状发作前22年就开始了。因此，这将是一段时间之前，这样的前瞻性研究产生足够的数据来阐明疾病的长期进展。该项目超越了ADNI等策划的数据集，并开发了新技术，可以利用常规收集的电子健康记录（EHR）数据对轻度认知障碍和AD诊断前后的患者轨迹进行新的分析。从观察数据中估计患者风险的工具有可能从AD推广到多年来进展缓慢的其他疾病。我们预计拟议的工作将通过识别最有可能从早期干预中受益的患者，为直接影响社会的临床系统奠定基础，并通过测量可改变的风险因素的影响来建议降低风险的行动。这项工作推进了机器学习（ML）领域在开发估计AD患者风险的工具的背景下，用于患者风险分层和个体治疗效果估计。在风险分层方面，用于在标签噪声存在下学习和用于多事件生存分析的新方法利用关于事件排序的约束的信息（例如，死亡不能先于AD）进行探索。此外，开发了新的ML技术，以提高我们使用观察数据估计因果效应的能力（例如，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

There is a pressing need to improve our ability to stratify and treat patients with or at risk of developing Alzheimer’s disease (AD). To this end, efforts like the Alzheimer’s Disease Neuroimaging Initiative (ADNI) aim to collect data on a wide range of biomarkers and have enrolled hundreds of participants who are followed longitudinally. While such studies are important and necessary, there is evidence to suggest that the development of AD starts as early as 22 years prior to symptom onset. Thus, it will be some time before such prospective studies produce enough data to shed light on the long-term progression of the disease. This project moves beyond curated datasets like ADNI and develops new techniques that can leverage routinely collected electronic health record (EHR) data for novel analyses of patient trajectories prior to and following a diagnosis with mild cognitive impairment and AD. Tools for estimating patient risk from observational data have the potential to generalize beyond AD to other conditions that progress slowly over the course of years. We expect the proposed work to lay the groundwork for clinical systems that directly impact society by identifying patients most likely to benefit from early intervention and recommend actions to reduce risk through measuring the effect of modifiable risk factors.This work advances the fields of machine learning (ML) for patient risk stratification and individual treatment effect estimation in the context of developing tools for estimating patient risk for AD. In terms of risk stratification, new approaches for learning in the presence of label noise and for multi-event survival analysis that leverage information about the constraints on the ordering of events (e.g., death cannot precede AD) are explored. In addition, novel ML techniques are developed to advance our ability to estimate causal effects using observational data (e.g., how does hypertension affect one’s risk of developing AD), with a focus on addressing bias related to confounding.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.