Explainable Artificial Intelligence for Predicting Gastrointestinal Adverse Effects of GLP-1 Receptor Agonists

Gastrointestinal (GI) adverse drug reactions (ADRs) are common among glucagon-like peptide-1 receptor agonist (GLP-1 RA) users and frequently contribute to treatment discontinuation and reduced therapeutic benefit. This retrospective study aimed to develop and validate an explainable artificial intelligence (XAI) model to predict GI ADR risk among GLP-1 RA users using real-world clinical data from the NIH All of Us Research Program. Adults prescribed GLP-1 RAs were identified and classified according to the occurrence of GI ADRs following treatment initiation. Multiple supervised machine learning models, including logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, neural network, LightGBM, and CatBoost, were evaluated using demographic, socioeconomic, clinical, medication, and laboratory variables. Model performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1-score. A total of 8697 participants were included, of whom 59.1% experienced GI ADRs. All models demonstrated reasonable predictive performance, with AUC values ranging from 0.82 to 0.84. The XGBoost achieved discrimination of (AUC: 0.84 ± 0.01). SHapley Additive exPlanations (SHAP) identified gastroesophageal reflux disease, hemorrhoids, and elevated HbA1c as important predictors of GI ADR risk. These findings demonstrate the potential utility of explainable machine learning approaches for predicting the safety of GLP-1 RA therapy.