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.

Interpretable coronary heart disease (CHD) risk prediction model based on the LightGBM gradient-boosting algorithm, combined with SHAP (SHapley Additive exPlanations) values to make individual risk predictions explainable to clinicians. Trained on the public BRFSS_2015 survey dataset and externally validated on the Framingham and Z-Alizadeh Sani datasets, the model reached 90.60% accuracy and 81.06% AUROC on BRFSS_2015, with SHAP analysis identifying age, smoking status, diabetes, hypertension, and high cholesterol as the most influential risk features. A companion CHD scoring system was built from the model to give clinicians a user-friendly risk-assessment tool.

Multimodal cardiac-sensing foundation model pretrained with generative masked pretraining on ECG, PPG, and paired clinical/machine-generated text reports from roughly 1.7 million individuals across three large-scale critical-care and outpatient ECG datasets. A channel-embedding scheme lets the same model accept any combination of 12-lead ECG, single-lead/wearable ECG, and PPG. The resulting embeddings transfer to diagnostic classification, demographic recognition, vital-sign measurement, clinical-outcome prediction, and ECG question answering. Pretrained weights require a signed academic-access agreement rather than an open download.