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.

数字健康技术，如可穿戴设备，通过个性化、连续和实时监测患者，为新型疗法和个体化医疗的发展做出了贡献。本文提出了一种新颖的药理学启发的神经随机微分方程模型，可以应对从这些技术中产生的具有随机特性的数据进行分析的挑战。使用合成数据，我们证明了我们的方法在识别治疗效果和学习因果关系方面是有效的，从而实现反事实模拟。