  We develop a deep learning model, ABioSPATH, to predict the one-year risk of
ischemic stroke (IS) in atrial fibrillation (AF) patients. The model integrates
drug-protein-disease pathways and real-world clinical data of AF patients to
generate the IS risk and potential pathways for each patient. The model uses a
multilayer network to identify the mechanism of drug action and disease
comorbidity propagation pathways. The model is tested on the Electronic Health
Record (EHR) data of 7859 AF patients from 43 hospitals in Hong Kong. The model
outperforms all baselines across all metrics and provides valuable
molecular-level insights for clinical use. The model also highlights key
proteins in common pathways and potential IS risks tied to less-studied drugs.
The model only requires routinely collected data, without requiring expensive
biomarkers to be tested.
