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

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dc.contributor.authorDara, Omer Nabeel
dc.contributor.authorMohammed, Tareq Abed
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.date.accessioned2025-08-25T13:15:10Z
dc.date.available2025-08-25T13:15:10Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.description.abstractHealthcare 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.
dc.identifier.citationDara, O. N., Mohammed, T. A., & Ibrahim, A. A. (2024). Evaluating the Effectiveness of Graph Convolutional Network for Detection of Healthcare Polypharmacy Side Effects. Intelligent Automation & Soft Computing, 39(6) 1007-1033. 10.32604/iasc.2024.058736
dc.identifier.doi10.32604/iasc.2024.058736
dc.identifier.endpage1033
dc.identifier.issn1079-8587
dc.identifier.issue6
dc.identifier.scopus2-s2.0-105012376353
dc.identifier.startpage1007
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5938
dc.identifier.volume39
dc.indekslendigikaynakScopus
dc.institutionauthorDara, Omer Nabeel
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.language.isoen
dc.publisherTech Science Press
dc.relation.ispartofIntelligent Automation and Soft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectdeep learning
dc.subjectdrug-drug interactions
dc.subjectgraph convolutional networks
dc.subjectmedication network
dc.subjectPolypharmacy
dc.subjectside effects
dc.titleEvaluating the Effectiveness of Graph Convolutional Network for Detection of Healthcare Polypharmacy Side Effects
dc.typeArticle

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