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

Duc Anh Nguyen*, Canh Hao Nguyen, Hiroshi Mamitsuka

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)
135 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)164-177
Number of pages14
JournalBriefings in Bioinformatics
Volume22
Issue number1
Early online date2019
DOIs
Publication statusPublished - Jan 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • ADR mechanism
  • ADR prediction
  • adverse drug reaction
  • machine learning methods

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