ADTEP (Adversarial Deep Treatment Effect Prediction)

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.