What is it about?
We developed a deep learning model that predicts the risk of stroke in patients with atrial fibrillation by combining clinical data with drug–protein–disease pathways. The model helps doctors better understand how medications and health conditions affect stroke risk for each individual.
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Perspectives
Current stroke prediction tools often overlook how medications and biological interactions influence outcomes. Our model integrates real-world data with molecular pathways, offering more accurate, personalized stroke risk assessments. It may also guide future research and improve clinical decision-making.
Zhongzhi Xu
Sun Yat-Sen University
Read the Original
This page is a summary of: Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug–protein–disease network-based deep learning, APL Bioengineering, April 2025, American Institute of Physics,
DOI: 10.1063/5.0242570.
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