Hypertension affects over 1.4 billion adults worldwide, and antihypertensive dose titration is a common but complex clinical decision. Although electronic health records contain longitudinal data on medication adjustments and blood pressure outcomes, determining optimal individualized dosing remains challenging due to confounding in observational data, where patients receiving higher doses often have worse baseline health. We propose a transformer-based model with causal attention masking to estimate counterfactual blood pressure outcomes under alternative dose regimens. The architecture ensures temporal validity by preventing information leakage from future events and encodes medication dose changes in a continuous representation. It includes a dose encoder, outcome predictor, and counterfactual contrastive loss to distinguish between competing treatment paths. This framework learns patient-specific dose–response relationships and enables personalized predictions for antihypertensive adjustments. While it supports individualized treatment planning from observational EHR data, prospective validation is still required before clinical deployment.
Clinical Intelligence Research Press