Toward multimodal signal detection of adverse drug reactions.

Journal of Biomedical Informatics
Rave HarpazNigam H Shah

Abstract

Improving mechanisms to detect adverse drug reactions (ADRs) is key to strengthening post-marketing drug safety surveillance. Signal detection is presently unimodal, relying on a single information source. Multimodal signal detection is based on jointly analyzing multiple information sources. Building on, and expanding the work done in prior studies, the aim of the article is to further research on multimodal signal detection, explore its potential benefits, and propose methods for its construction and evaluation. Four data sources are investigated; FDA's adverse event reporting system, insurance claims, the MEDLINE citation database, and the logs of major Web search engines. Published methods are used to generate and combine signals from each data source. Two distinct reference benchmarks corresponding to well-established and recently labeled ADRs respectively are used to evaluate the performance of multimodal signal detection in terms of area under the ROC curve (AUC) and lead-time-to-detection, with the latter relative to labeling revision dates. Limited to our reference benchmarks, multimodal signal detection provides AUC improvements ranging from 0.04 to 0.09 based on a widely used evaluation benchmark, and a comparative a...Continue Reading

Citations

Oct 28, 2019·Drug Safety : an International Journal of Medical Toxicology and Drug Experience·Justin MowerDevika Subramanian
Aug 26, 2019·Drug Safety : an International Journal of Medical Toxicology and Drug Experience·John van StekelenborgMunir Pirmohamed
Jul 17, 2018·Journal of the American Medical Informatics Association : JAMIA·Justin MowerTrevor Cohen
May 10, 2020·Drug Safety : an International Journal of Medical Toxicology and Drug Experience·Ying LiCao Xiao

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