Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework.

ABSTRACT: Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing computational methods primarily focus on predicting whether or not the drug is associated with an adverse reaction and do not consider the core issue of drug benefit-risk assessment-whether the treatment outcome is serious when adverse drug reactions occur. To this end, we categorize serious clinical outcomes caused by adverse reactions to drugs into seven distinct classes and present a deep learning framework, so-called GCAP, for predicting the seriousness of clinical outcomes of adverse reactions to drugs. GCAP has two tasks: one is to predict whet

SUBMITTER: Zhao H 

PROVIDER: S-EPMC10449791 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature