Machine learning to predict adverse events of sedatives in gastrointestinal endoscopy by using a random forest model

Aims Sedation has become standard practice for patients undergoing gastrointestinal (GI) endoscopy. However, considering the serious cardiopulmonary adverse events (AEs) of sedatives, it is important to identify patients at high risk. Nowadays, machine learning across a wide range of medical data can generate reasonable predictions for AEs in the clinical field. This study aims to perform a machine learning using a random forest model to identify predictors of AEs in sedative GI endoscopy.