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

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.