Omer Nabeel Dara1,*, Tareq Abed Mohammed2, Abdullahi Abdu Ibrahim1
Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 1007-1033, 2024, DOI:10.32604/iasc.2024.058736
- 30 December 2024
Abstract 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 More >
Login
Register
