A pipeline to extract drug-adverse event pairs from multiple data sources

BMC Medical Informatics and Decision Making

Research Article

Aditya Rao¹  Thomas Joseph¹  Vangala Govindakrishnan Saipradeep¹  Rajgopal Srinivasan¹  SriJyothsna Yeleswarapu¹ 

[1] TCS Innovation Labs, Tata Consultancy Services Ltd, Deccan Park, 1, Software Units Layout, Madhapur, 500081, Hyderabad, Andhra Pradesh, India;

关键词: Pharmacovigilance;    NLP;    Text mining;    Social media;    Adverse event;    Biomedical literature;    Unstructured text;    BCPNN;   

DOI  :  10.1186/1472-6947-14-13

 received in 2013-06-11, accepted in 2014-02-14,  发布年份 2014

来源: Springer

【 摘 要 】

BackgroundPharmacovigilance 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.MethodWe 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.ResultsTesting 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.ConclusionA 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.

【 授权许可】

Unknown   
© Yeleswarapu et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.