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Review
. 2021 Jan 18;22(1):164-177.
doi: 10.1093/bib/bbz140.

A survey on adverse drug reaction studies: data, tasks and machine learning methods

Duc Anh Nguyen  1 , Canh Hao Nguyen  2 , Hiroshi Mamitsuka  2
Affiliations
Free article
Review

A survey on adverse drug reaction studies: data, tasks and machine learning methods

Duc Anh Nguyen et al. Brief Bioinform. .
Free article

Abstract

Motivation: Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine learning methods have emerged as prominent tools to support analyzing and predicting ADRs. Nonetheless, there are still remaining challenges in ADR studies.

Results: In this paper, we summarized ADR data sources and review ADR studies in three tasks: drug-ADR benchmark data creation, drug-ADR prediction and ADR mechanism analysis. We focused on machine learning methods used in each task and then compare performances of the methods on the drug-ADR prediction task. Finally, we discussed open problems for further ADR studies.

Availability: Data and code are available at https://github.com/anhnda/ADRPModels.

Keywords: ADR mechanism; ADR prediction; adverse drug reaction; machine learning methods.

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