AI copilot of personalized risk education for post-revascularization care in patients with coronary artery disease (CAD)
H Yu, Y Zhang, L Lui, L Fan, X Ma, G M Tan, B Yan, K SoAbstract
Background
Coronary artery disease (CAD) remains a major health concern, and patients after coronary revascularization face elevated risk of repeat revascularization or myocardial infarction (MI)1. However, risk communication and education are often difficult for patients to understand and act on2. AI-based tools may improve engagement by delivering personalized, clear, and actionable explanations of individual risk factors3.
Purpose
To develop and validate an AI-powered chatbot that provides individualized risk communication and patient education for CAD patients after revascularization, leveraging an Electronic Health Records (EHR)-based prediction model to explain personal risk and support informed care decisions.
Methods
We analyzed EHR records for 26,746 patients who underwent coronary revascularization between 2021 and 2025; 6,727 had repeat revascularization or diagnosed MI during follow-up. Median age was 69.2 years (IQR: 62.3–76.2), and 77.3% were male. Predictors included demographics, key clinical history (e.g., prior MI / angina / heart failure / stroke / hypertension / diabetes / chronic kidney disease), and laboratory markers (e.g., glucose, HDL, LDL) summarized in pre- and post-operative time windows (e.g., 0–14, 14–30 days). We trained multiple machine-learning models (logistic regression, random forest, XGBoost, SVM, LightGBM). SHapley Additive exPlanations (SHAP) provided patient-level interpretability8, and a large language model (LLM)-powered chatbot generated patient-friendly explanations by integrating model outputs with EHR context in a web interface.
Results
The system produced personalized risk explanations that highlighted patient-specific drivers and offered actionable lifestyle modification suggestions. The LightGBM model showed strong discrimination (AUC 0.862, accuracy 84.7%, F1 56.73%) and SHAP waterfall plots improved transparency by visualizing contributions from features such as age, comorbidities, and post-operative biomarkers. For instance, chronic ischemic heart disease and prior MI substantially increased predicted risk, while post-operative changes in white blood cell count and hemoglobin showed time-window–dependent effects. The LLM-powered chatbot translated these insights into understandable narratives, supporting patient comprehension and engagement. The web-based interface enabled intuitive interaction for both patients and clinicians.
Conclusions
An AI-powered chatbot for post-revascularization CAD risk communication is feasible and scalable. An EHR-trained predictive model can quantify risk, while SHAP improves interpretability and trust. The LLM-powered chatbot provides personalized education that may empower patients to make informed decisions. Future work will prioritize clinical validation, user experience testing, and clinical trials assessing long-term outcomes to establish effectiveness in routine care.