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

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2021-01

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Department of Computer Science

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Language

en

Pages

14
164-177

Series

BRIEFINGS IN BIOINFORMATICS, Volume 22, issue 1

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.

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Publisher Copyright: © 2019 The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

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

Other note

DOI

10.1093/bib/bbz140

Citation

Nguyen, D A, Nguyen, C H & Mamitsuka, H 2021, ' A survey on adverse drug reaction studies : Data, tasks and machine learning methods ', Briefings in Bioinformatics, vol. 22, no. 1, pp. 164-177 . https://doi.org/10.1093/bib/bbz140

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https://urn.fi/URN:NBN:fi:aalto-202208104796

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[article-cris] Perustieteiden korkeakoulu / SCI