  Digital health technologies (DHT), such as wearable devices, provide
personalized, continuous, and real-time monitoring of patient. These
technologies are contributing to the development of novel therapies and
personalized medicine. Gaining insight from these technologies requires
appropriate modeling techniques to capture clinically-relevant changes in
disease state. The data generated from these devices is characterized by being
stochastic in nature, may have missing elements, and exhibits considerable
inter-individual variability - thereby making it difficult to analyze using
traditional longitudinal modeling techniques. We present a novel
pharmacology-informed neural stochastic differential equation (SDE) model
capable of addressing these challenges. Using synthetic data, we demonstrate
that our approach is effective in identifying treatment effects and learning
causal relationships from stochastic data, thereby enabling counterfactual
simulation.
