Evaluating the Effectiveness of Graph Convolutional Network for Detection of Healthcare Polypharmacy Side Effects.

Dara, Omer Nabeel; Mohammed, Tareq Abed; Ibrahim, Abdullahi Abdu

Healthcare polypharmacy is routinely used to treat numerous conditions; however, it often leads to unanticipated bad consequences owing to complicated medication interactions. This paper provides a graph convolutional network (GCN)-based model for identifying adverse effects in polypharmacy by integrating pharmaceutical data from electronic health records (EHR). The GCN framework analyzes the complicated links between drugs to forecast the possibility of harmful drug interactions. Experimental assessments reveal that the proposed GCN model surpasses existing machine learning approaches, reaching an accuracy (ACC) of 91%, an area under the receiver operating characteristic curve (AUC) of 0.88, and an F1-score of 0.83. Furthermore, the overall accuracy of the model achieved 98.47%. These findings imply that the GCN model is helpful for monitoring individuals receiving polypharmacy. Future research should concentrate on improving the model and extending datasets for therapeutic applications.

RECEIVER operating characteristic curves; DRUG interactions; ELECTRONIC health records; DEEP learning; POLYPHARMACY

Intelligent Automation & Soft Computing, 2024, Vol 39, Issue 6, p1007

1079-8587

Academic Journal

10.32604/iasc.2024.058736