A curated and standardized adverse drug event resource to accelerate drug safety research

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Identification of adverse drug reactions (ADRs) during the post-marketing phase is one of the most important goals of drug safety surveillance. Spontaneous reporting systems (SRS) data, which are the mainstay of traditional drug safety surveillance, are used for hypothesis generation and to validate the newer approaches. The publicly available US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) data requires substantial curation before they can be used appropriately, and applying different strategies for data cleaning and normalization can have material impact on analysis results. We provide a curated and standardized version of FAERS removing duplicate case records, applying standardized vocabularies with drug names mapped to RxNorm concepts and outcomes mapped to SNOMED-CT concepts, and pre-computed summary statistics about drug-outcome relationships for general consumption. This publicly available resource, along with the source code, will accelerate drug safety research by reducing the amount of time spent performing data management on the source FAERS reports, improving the quality of the underlying data, and enabling standardized analyses using common vocabularies.

Related collections

Most cited references 29

Record: found
Abstract: found
Article: not found

Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports.

S J Evans, P Waller, S. Davis (2002)

The process of generating 'signals' of possible unrecognized hazards from spontaneous adverse drug reaction reporting data has been likened to looking for a needle in a haystack. However, statistical approaches to the data have been under-utilised. Using the UK Yellow Card database, we have developed and evaluated a statistical aid to signal generation called a Proportional Reporting Ratio (PRR). The proportion of all reactions to a drug which are for a particular medical condition of interest is compared to the same proportion for all drugs in the database, in a 2 x 2 table. We investigated a group of newly-marketed drugs using as minimum criteria for a signal, 3 or more cases, PRR at least 2, chi-squared of at least 4. The database was used to examine retrospectively 15 drugs newly-marketed in the UK, with the highest levels of ADR reporting. The method identified 481 signals meeting the minimum criteria during the period 1996-8. Further evaluation of these showed that 70% were known adverse reactions, 13% were events which were likely to be related to the underlying disease and 17% were signals requiring further evaluation. Proportional reporting ratios are a valuable aid to signal generation from spontaneous reporting data which are easy to calculate and interpret, and various refinements are possible.

0 comments Cited 317 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Is Open Access

Utilizing social media data for pharmacovigilance: A review.

Abeed Sarker, Rachel E. Ginn, Azadeh Nikfarjam … (2015)

Automatic monitoring of Adverse Drug Reactions (ADRs), defined as adverse patient outcomes caused by medications, is a challenging research problem that is currently receiving significant attention from the medical informatics community. In recent years, user-posted data on social media, primarily due to its sheer volume, has become a useful resource for ADR monitoring. Research using social media data has progressed using various data sources and techniques, making it difficult to compare distinct systems and their performances. In this paper, we perform a methodical review to characterize the different approaches to ADR detection/extraction from social media, and their applicability to pharmacovigilance. In addition, we present a potential systematic pathway to ADR monitoring from social media.

0 comments Cited 150 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features

Azadeh Nikfarjam, Abeed Sarker, Karen O’Connor … (2015)

Objective Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. Methods We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words’ semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. Results ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. Conclusion It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets.

0 comments Cited 132 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-ta): Sci Data

Journal ID (iso-abbrev): Sci Data

Title: Scientific Data

Publisher: Nature Publishing Group

ISSN (Electronic): 2052-4463

Publication date (Electronic): 10 May 2016

Publication date Collection: 2016

Volume: 3

Electronic Location Identifier: 160026

Affiliations

[1 ] Center for Biomedical Informatics Research, Stanford University , Stanford, California 94305, USA

[2 ] LTS Computing LLC , West Chester, Pennsylvania 19380, USA

[3 ] Department of Biomedical Informatics, Columbia University , New York, New York 10032, USA

[4 ] Janssen Research & Development, LLC , Titusville, New Jersey 08869, USA

Author notes

[a ]J.M.B. (email: jmbanda@ 123456stanford.edu ).

[]

J.M.B. performed the quality checking, participated in design review, verified usability and wrote the paper. L.E. implemented the cleanup process, led the design review, and contributed to the paper. R.S.V. contributed to the paper. N.P.T. participated in design review, and contributed to the schema. P.R. conceived of the project, and wrote the paper. N.H. conceived of the project, participated in design review, and wrote the paper.

Author information

Juan M. Banda http://orcid.org/0000-0001-8499-824X

Article

Publisher Item ID: sdata201626

DOI: 10.1038/sdata.2016.26

PMC ID: 4872271

PubMed ID: 27193236

SO-VID: 365644d8-d501-4ff1-a51e-26f71d12ead4

License:

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse.

A curated and standardized adverse drug event resource to accelerate drug safety research

Read this article at

Abstract

Related collections

Drug Repurposing Research Collection

Most cited references 29

Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports.

Utilizing social media data for pharmacovigilance: A review.

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features

Author and article information

Journal

Affiliations

Author notes

Author information

Article

History

Categories

Comments

Comment on this article

Similar content 31

Cited by 69

Most referenced authors 241