A Recommender System Approach for Predicting Drug Side Effects

dc.contributor.author	Galeano Galeano, Diego Ariel
dc.contributor.author	Paccanaro, Alberto
dc.date.accessioned	2022-04-21T02:21:26Z
dc.date.available	2022-04-21T02:21:26Z
dc.date.issued	2018
dc.identifier.uri	http://hdl.handle.net/20.500.14066/2996
dc.description.abstract	The accurate identification of drug side effects represents a major concern for public health. We propose a collaborative filtering model for large-scale prediction of drug side effects. Our approach provides side effects recommendations for drugs to safety professionals. The proposed latent factor model relies solely on the public drug-side effect relationships from safety data.	es
dc.description.sponsorship	CONACYT – Consejo Nacional de Ciencia y Tecnología	es
dc.language.iso	eng	es
dc.subject.classification	6 Producción y tecnología industrial	es
dc.subject.other	DRUG	es
dc.subject.other	SIDE EFFECTS	es
dc.subject.other	RECOMMENDATION SYSTEMS	es
dc.subject.other	ADVERSE DRUG EVENTS	es
dc.subject.other	LATENT FACTOR MODEL	es
dc.subject.other	COLLABORATIVE FILTERING	es
dc.title	A Recommender System Approach for Predicting Drug Side Effects	es
dc.type	research article	es
dc.identifier.doi	10.1109/IJCNN.2018.8489025	es
dc.relation.projectCONACYT	14-INV-088	es
dc.rights.accessRights	open access	es
dc.subject.ocde	FARMACOLOGIA	es

Artículos científicos
La colección comprende artículos científicos, revisiones y artículos de conferencia que son resultados de actividades científicas y de innovación financiadas por los programas PROCIENCIA y PROINNOVA.