A Recommender System Approach for Predicting Drug Side Effects

dc.creatorGaleano Galeano, Diego Ariel
dc.creatorPaccanaro, Alberto
dc.date2022-04-21T02:21:26Z
dc.date2022-04-21T02:21:26Z
dc.date2018
dc.date.accessioned2023-09-25T13:31:46Z
dc.date.available2023-09-25T13:31:46Z
dc.identifierhttp://hdl.handle.net/20.500.14066/2996
dc.identifier10.1109/IJCNN.2018.8489025
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8807612
dc.descriptionThe 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.
dc.descriptionCONACYT – Consejo Nacional de Ciencia y Tecnología
dc.languageeng
dc.relation14-INV-088
dc.rightsopen access
dc.subject6 Producción y tecnología industrial
dc.subjectDRUG
dc.subjectSIDE EFFECTS
dc.subjectRECOMMENDATION SYSTEMS
dc.subjectADVERSE DRUG EVENTS
dc.subjectLATENT FACTOR MODEL
dc.subjectCOLLABORATIVE FILTERING
dc.subjectFARMACOLOGIA
dc.titleA Recommender System Approach for Predicting Drug Side Effects
dc.typeresearch article


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