Generative Modeling of Clinical Time Series via Latent Stochastic Differential Equations

Clinical time series data from electronic health records and medical registries offer unprecedented opportunities to understand patient trajectories and inform medical decision-making. However, leveraging such data presents significant challenges due to irregular sampling, complex latent physiology, and inherent uncertainties in both measurements and disease progression. To address these challenges, we propose a generative modeling framework based on latent neural stochastic differential equations (SDEs) that views clinical time series as discrete-time partial observations of an underlying controlled stochastic dynamical system. This formulation naturally handles irregularly sampled observations, learns complex non-linear interactions, and captures the stochasticity of disease progression and measurement noise within a unified scalable probabilistic framework. Results show that our framework outperforms ordinary differential equation and long short-term memory baseline models in accuracy and uncertainty estimation. These results highlight its potential for enabling precise, uncertainty-aware predictions to support clinical decision-making. Introduction Predicting patient trajectories is critical for enabling timely interventions, better understanding of disease progression, and developing personalized medicine [1]. For instance, early detection of sepsis has been shown to significantly reduce the risk of organ failure and mortality [2]. This potential is increasingly becoming feasible due to the rapid growth of available healthcare data like electronic health records (EHRs) [3]. A defining feature of healthcare data are their temporal nature, reflecting the dynamic evolution of patient conditions over time. These temporal patterns highlight the need for time series models specifically tailored to the complexities of clinical data. However, healthcare time series have unique characteristics such as missing values, irregular sampling, aleatoric uncertainty, and patient-specific variability, that make modeling them particularly challenging [5, 6]. Traditional time series models, such as autoregressive moving average (ARIMA) models, have been applied to healthcare data but often struggle with its complexity and irregularity [7].