Debiased EHR Embeddings for Individualized Treatment Effect Estimation in Precision Medicine

Music City Center 

Leveraging real-world electronic health records (EHR) for precision medicine requires robust modeling of patient heterogeneity and treatment effects while mitigating biases inherent in observational data. We introduce a novel framework for learning rich, contextualized, and debiased EHR embeddings that enable individualized counterfactual outcome prediction and precise estimation of individualized treatment effects (ITE). Our approach integrates adversarial debiasing and negative control strategies to correct for confounding while preserving patient-specific contextual information. We demonstrate its utility in optimizing the use of GLP-1 receptor agonists (GLP-1RAs), identifying patients who would benefit but are currently untreated, and detecting those receiving treatment despite being suboptimal candidates for heart failure and mental health outcomes. This method provides a robust foundation for precision medicine, ensuring treatment decisions are data-driven, patient-specific, and causally robust.

Counterfactual Outcome Prediction


Negative control outcomes

Precision Medicine


Real-World Evidence


electronic health records

Individualized Treatment Effect (ITE) 

Biometrics Section