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

作　　者：Omer Nabeel Dara Tareq Abed Mohammed Abdullahi Abdu Ibrahim 

机构地区：[1]Collage of Engineering,Department of Electrical and Computer Engineering,Altinbas University,Istanbul,34000,Turkey [2]College of Computer Science and Information Technology,Department of Information Technology,University of Kirkuk,Kirkuk,36001,Iraq

出　　处：《Intelligent Automation & Soft Computing》2024年第6期1007-1033,共27页智能自动化与软计算（英文）

摘　　要：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.

关 键 词：POLYPHARMACY side effects drug-drug interactions graph convolutional networks deep learning medication network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] R97[自动化与计算机技术—控制科学与工程]