Generative Modeling of Clinical Time Series via Latent Stochastic Differential Equations

TL;DR

This paper introduces a novel generative modeling framework using latent neural stochastic differential equations to analyze irregular, uncertain clinical time series data, improving prediction accuracy and uncertainty quantification for medical decision-making.

Contribution

It presents a scalable probabilistic model that captures complex, non-linear, and stochastic dynamics of clinical data, addressing challenges of irregular sampling and measurement noise.

Findings

Outperforms ODE and LSTM models in accuracy and uncertainty estimation.

Effectively models disease progression and physiological signals.

Validated on simulated and real-world ICU data with 12,000 patients.

Abstract

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. Our approach models latent dynamics via neural SDEs with modality-dependent emission models, while performing state estimation and parameter learning through variational inference. 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. We validate the framework on two complementary tasks: (i) individual treatment effect estimation using a simulated pharmacokinetic-pharmacodynamic (PKPD) model of lung cancer, and (ii) probabilistic forecasting of physiological signals using real-world intensive care unit (ICU) data from 12,000 patients. 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.