Omer Nabeel Dara^1,*, 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 >

Khalid Alzahrani^1,*, Mrim Alnfiai²

Computer Systems Science and Engineering, Vol.41, No.2, pp. 445-460, 2022, DOI:10.32604/csse.2022.020620 - 25 October 2021

Abstract A complete and thorough understanding by patients of their prescriptions is one of the most critical components of a successful treatment journey. Being unfamiliar with the intricacies of prescribed medication can cause serious health risks due to not adhering to prescription instructions or noting potential drug interactions, which can lead to life-threatening injuries. Pharmacists face communication barriers (including non-English speaking patients), lack of time, lack of knowledge, workload, and frequent interruption when dispensing medicines often preventing them from providing the necessary guidance to their patients. To minimize this risk, an NFC-Guidable polypharmacy system was developed… More >