Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods

TL;DR

This paper introduces a deep learning approach combining differential equations and neural networks to model individual-level latent dynamics with changing parameters across observation periods, improving resilience factor prediction.

Contribution

It extends existing models by allowing differential equation parameters to vary between sub-periods, enabling more flexible and accurate individual dynamic modeling.

Findings

Successfully identified individual-level dynamic parameters.

Enabled stable predictor selection for resilience factors.

Improved identification of promising characteristics for follow-up updates.

Abstract

When modeling longitudinal biomedical data, often dimensionality reduction as well as dynamic modeling in the resulting latent representation is needed. This can be achieved by artificial neural networks for dimension reduction, and differential equations for dynamic modeling of individual-level trajectories. However, such approaches so far assume that parameters of individual-level dynamics are constant throughout the observation period. Motivated by an application from psychological resilience research, we propose an extension where different sets of differential equation parameters are allowed for observation sub-periods. Still, estimation for intra-individual sub-periods is coupled for being able to fit the model also with a relatively small dataset. We subsequently derive prediction targets from individual dynamic models of resilience in the application. These serve as interpretable resilience-related outcomes, to be predicted from characteristics of individuals, measured at baseline and a follow-up time point, and selecting a small set of important predictors. Our approach is seen to successfully identify individual-level parameters of dynamic models that allows us to stably select predictors, i.e., resilience factors. Furthermore, we can identify those characteristics of individuals that are the most promising for updates at follow-up, which might inform future study design. This underlines the usefulness of our proposed deep dynamic modeling approach with changes in parameters between observation sub-periods.