A Pipeline to Extract Drug-adverse Event Pairs from Multiple Data Sources

Overview

Journal BMC Med Inform Decis Mak

Publisher Biomed Central

Specialty Medical Informatics

Date 2014 Feb 25

PMID 24559132

Citations 22

Authors

Srijyothsna Yeleswarapu

Aditya Rao

Thomas Joseph

Vangala Govindakrishnan Saipradeep

Rajgopal Srinivasan

Affiliations

Soon will be listed here.

Abstract

Background: Pharmacovigilance aims to uncover and understand harmful side-effects of drugs, termed adverse events (AEs). Although the current process of pharmacovigilance is very systematic, the increasing amount of information available in specialized health-related websites as well as the exponential growth in medical literature presents a unique opportunity to supplement traditional adverse event gathering mechanisms with new-age ones.

Method: We present a semi-automated pipeline to extract associations between drugs and side effects from traditional structured adverse event databases, enhanced by potential drug-adverse event pairs mined from user-comments from health-related websites and MEDLINE abstracts. The pipeline was tested using a set of 12 drugs representative of two previous studies of adverse event extraction from health-related websites and MEDLINE abstracts.

Results: Testing the pipeline shows that mining non-traditional sources helps substantiate the adverse event databases. The non-traditional sources not only contain the known AEs, but also suggest some unreported AEs for drugs which can then be analyzed further.

Conclusion: A semi-automated pipeline to extract the AE pairs from adverse event databases as well as potential AE pairs from non-traditional sources such as text from MEDLINE abstracts and user-comments from health-related websites is presented.

Citing Articles

Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study.

Karapetiantz P, Audeh B, Redjdal A, Tiffet T, Bousquet C, Jaulent M J Med Internet Res. 2024; 26:e46176.

PMID: 38888956 PMC: 11220433. DOI: 10.2196/46176.

Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data.

Lee S, Woo H, Lee C, Kim G, Kim J, Lee S Sci Rep. 2023; 13(1):3779.

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LiSA: an assisted literature search pipeline for detecting serious adverse drug events with deep learning.

Martenot V, Masdeu V, Cupe J, Gehin F, Blanchon M, Dauriat J BMC Med Inform Decis Mak. 2022; 22(1):338.

PMID: 36550485 PMC: 9773506. DOI: 10.1186/s12911-022-02085-0.

Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers.

Gonzalez-Hernandez G, Krallinger M, Munoz M, Rodriguez-Esteban R, Uzuner O, Hirschman L Database (Oxford). 2022; 2022.

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Discovering symptom patterns of COVID-19 patients using association rule mining.

Tandan M, Acharya Y, Pokharel S, Timilsina M Comput Biol Med. 2021; 131:104249.

PMID: 33561673 PMC: 7966840. DOI: 10.1016/j.compbiomed.2021.104249.

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