The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Data Mining of the Public Version of the FDA Adverse Event Reporting System
Open accessAbstract
The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS, formerly AERS) is a database that contains information on adverse event and medication error reports submitted to the FDA. Besides those from manufacturers, reports can be submitted from health care professionals and the public. The original system was started in 1969, but since the last major revision in 1997, reporting has markedly increased. Data mining algorithms have been developed for the quantitative detection of signals from such a large database, where a signal means a statistical association between a drug and an adverse event or a drug-associated adverse event, including the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM). A survey of our previous reports suggested that the ROR provided the highest number of signals, and the EBGM the lowest. Additionally, an analysis of warfarin-, aspirin- and clopidogrel-associated adverse events suggested that all EBGM-based signals were included in the PRR-based signals, and also in the IC- or ROR-based ones, and that the PRR- and IC-based signals were in the ROR-based ones. In this article, the latest information on this area is summarized for future pharmacoepidemiological studies and/or pharmacovigilance analyses.
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Wang, F., & Preininger, A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677908 · Cited by 398
Artificial Intelligence for Drug Toxicity and Safety
Basile, A. O., Yahi, A., & Tatonetti, N. P. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.07.005 · Cited by 363
Gastaldon, C., Raschi, E., Kane, J. M., Barbui, C., & Schoretsanitis, G. (2021) · Psychotherapy and Psychosomatics · doi:10.1159/000510703 · Cited by 134
Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance
Raji, I. D., Xu, P., Honigsberg, C., & Ho, D. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3514094.3534181 · Cited by 209
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Metadata
- Authors
- Sakaeda, T., Tamon, A., Kadoyama, K., & Okuno, Y.
- Venue
- International Journal of Medical Sciences
- Publisher
- Ivyspring International Publisher
- Published
- 2013
- Volume
- 10
- Issue
- 7
- Pages
- 796-803
- ISSN
- 1449-1907 (ISSN-L)
- OpenAlex
- W2113514677
- Cited by
- 1,048
- References
- 76
- FWCI
- 26.91
- Type
- journal article
- Language
- en
- Open access
- gold
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- Sakaeda, T., Tamon, A., Kadoyama, K., & Okuno, Y. (2013). Data Mining of the Public Version of the FDA Adverse Event Reporting System. International Journal of Medical Sciences. https://doi.org/10.7150/ijms.6048
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