  Longitudinal patient data has the potential to improve clinical risk
stratification models for disease. However, chronic diseases that progress
slowly over time are often heterogeneous in their clinical presentation.
Patients may progress through disease stages at varying rates. This leads to
pathophysiological misalignment over time, making it difficult to consistently
compare patients in a clinically meaningful way. Furthermore, patients present
clinically for the first time at different stages of disease. This eliminates
the possibility of simply aligning patients based on their initial
presentation. Finally, patient data may be sampled at different rates due to
differences in schedules or missed visits. To address these challenges, we
propose a robust measure of patient similarity based on subsequence alignment.
Compared to global alignment techniques that do not account for
pathophysiological misalignment, focusing on the most relevant subsequences
allows for an accurate measure of similarity between patients. We demonstrate
the utility of our approach in settings where longitudinal data, while useful,
are limited and lack a clear temporal alignment for comparison. Applied to the
task of stratifying patients for risk of progression to probable Alzheimer's
Disease, our approach outperforms models that use only snapshot data (AUROC of
0.839 vs. 0.812) and models that use global alignment techniques (AUROC of
0.822). Our results support the hypothesis that patients' trajectories are
useful for quantifying inter-patient similarities and that using subsequence
matching and can help account for heterogeneity and misalignment in
longitudinal data.
