Interventional Flow Matching: Prospective Dose-Response Forecasting with Velocity-Field Jacobian Regularization

2026-06-28

AI Summary

This work addresses the challenge of prospectively forecasting patient physiological trajectories under planned interventions—such as insulin administration and carbohydrate intake—by proposing Interventional Flow Matching (IFM), a continuous-time generative model. IFM models glucose dynamics within a bounded latent space, conditioning the velocity field on both historical patient states and future interventions. It introduces a novel solver-free Jacobian regularization that directly constrains the local sensitivity of the velocity field to treatment variables, thereby enforcing physiologically plausible directions and magnitudes of glycemic response (e.g., insulin-induced glucose reduction and carbohydrate-driven elevation). Without relying on explicit differential equations or rollout simulations, IFM achieves an optimal trade-off between observational noise and intervention responsiveness in the UVA/Padova Type 1 diabetes simulator cohort, consistently generating dose–response relationships that align with known physiology in both directionality and ranking consistency.

Abstract

Predicting a patient's physiological trajectory under a planned treatment sequence is a prospective interventional problem, not standard time-series extrapolation. We study this problem in glucose management, where insulin and carbohydrate records are policy-dependent: future drivers are coupled to patient state, behavior, and clinical decision rules, so observational forecasting accuracy alone does not guarantee correct responses to planned interventions. We introduce Interventional Flow Matching (IFM), a continuous-time generative framework for physiologically constrained prospective forecasting. IFM conditions a flow-matching velocity field on patient history and planned future drivers in a bounded latent glucose space. Rather than embedding strict mechanistic glucose--insulin ODE equations or enforcing causality through rollout-based simulations, IFM uses a solver-free regularization: it penalizes the Jacobian of the instantaneous velocity field with respect to smoothed treatment drivers. This imposes signed, dose-bounded local sensitivities directly on the learned dynamics: insulin lowers glucose, carbohydrates raise it, and both responses remain within plausible ranges. On a simulated UVA/Padova type 1 diabetes cohort, IFM achieves the strongest balance between observed-driver RMSE and interventional response metrics. Across experiments, it consistently produces physiologically correct responses to both insulin and carbohydrate drivers while maintaining high directional, and ranking consistency.

Problem

• interventional forecasting
• dose-response prediction
• glucose management
• treatment planning
• physiological trajectory

Innovation

• Interventional Flow Matching
• Jacobian regularization
• prospective forecasting
• dose-response modeling
• physiological dynamics