Predictive Model of Internal Bleeding in Elderly Aspirin Users Using XGBoost Machine Learning.

Tenggao ChenWanlin LeiMaofeng Wang

Published in: Risk management and healthcare policy (2024)

This study successfully developed a predictive model to estimate the risk of bleeding in elderly aspirin users. This model can serve as a potential useful tool for clinicians to estimate the risk of bleeding in elderly aspirin users and make informed decisions regarding their treatment and management.

Keyphrases

low dose
machine learning
atrial fibrillation
middle aged
cardiovascular events
antiplatelet therapy
community dwelling
cardiovascular disease
acute coronary syndrome
climate change