Machine learning may help identify patients at high risk of GI bleeding: JAMA

In a recent cross-sectional study, machine learning models were examined, showing similar performance in identifying patients at high risk for GIB after being prescribed antithrombotic agents. Findings have been published in JAMA Network Open. "Two models (RegCox and XGBoost) performed modestly better than the HAS-BLED score. A prospective evaluation of the RegCox model compared with HAS-BLED may provide a better understanding of the clinical impact of improved performance."the Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic treatment (oral antiplatelets or anticoagulants) is limited by existing risk prediction models.