Evaluating The Effectiveness Of Graph Convolutional Network For Detection Of Polypharmacy Side Effects

Abstract

Polypharmacy is frequently used to treat numerous illnesses; however, it might have unintended consequences. Recent research using graph convolutional networks (GCNs) has demonstrated promising results in the difficult problem of polypharmacy side effect detection. Using accuracy (ACC), area under the receiver operating characteristic curve (AUC), and F1-score, this study presents a GCN-based model for detecting polypharmacy side effects. The model draws on pharmaceutical data from electronic health records to generate a medication network graph to learn the intricate interrelationships between medications and estimate the risk of adverse effects. The experimental results demonstrate that the proposed GCN-based model outperforms conventional machine learning techniques with an ACC of 91%, an AUC of 0.88, and an F1-score of 0.83. The findings of this article suggest that GCNs are a useful method for monitoring patients on several medications for adverse reactions with total accuracy of 98.47%. Additional study is required to refine the model and collect more data for use in actual clinical settings.