Adversarial deep learning model that predicts treatment effects for cardiology patients from structured electronic health record (EHR) data, aiming to forecast expected clinical outcomes of specific treatment choices given a patient's clinical status. Two autoencoders separately learn representations of patient characteristics and of the treatments given; an adversarial loss then encourages these representations to capture the correlational structure between a patient's status and the treatment received, improving downstream outcome prediction over non-adversarial baselines. Evaluated on two private cardiology EHR cohorts from a Chinese hospital, ADTEP modestly outperformed a non-adversarial ablation (DTEP) and classical baselines (logistic regression, SVM) at predicting major adverse cardiac events (MACE) after acute coronary syndrome (AUC 0.662 vs. 0.653/0.648/0.621) and at heart-failure outcome prediction.