副作用(计算机科学)
计算机科学
药品
协同过滤
机器学习
鉴定(生物学)
推荐系统
召回
数据挖掘
人工智能
医学
药理学
心理学
认知心理学
生物
程序设计语言
植物
作者
Diego Galeano,Alberto Paccanaro
标识
DOI:10.1109/ijcnn.2018.8489025
摘要
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. Applied to 1,525 marketed drugs and 2,050 side effect terms, we achieved an AUPRC (area under the precision- recall curve) of 0.342 in a test set, with a sensitivity of 0.73 given a specificity of 0.95, providing state-of-the-art performance in side effect prediction. We analyze the performance of the method on drug-specific Anatomical Therapeutic and Chemical (ATC) category and side effect- specific medical category of disorders. Our findings suggest that latent factor models can be useful for the early and accurate detection of unknown adverse drug events.
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