With more patients taking multiple medications and the increasing digital availability of diagnostic data such as treatment notes and x-ray images, the importance of decision support systems to help dentists in their treatment planning cannot be over emphasised. Based on the hypothesis that a higher similarity ratio between drugs in a drug-pair indicates that the combination of the drug-pair has a higher chance of an adverse interaction, this paper describes an efficient approach in extracting feature vectors from the drugs in a drug-pair to compute the similarity ratio between them. The feature vectors are obtained through a network model where the information of the drugs are represented as nodes and the relationships between them represented as edges. Experimental evaluation of our model yielded a superior F score of 74%. The use of a network model will drive research efforts into more efficient data-mining algorithms for information retrieval, similarity search and machine learning. Since it is important to avoid drug allergies when prescribing drugs, our work when integrated within the clinical work-flow will reduce prescription errors thereby increasing health outcomes for patients.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.