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      Postmarket Drug Surveillance Without Trial Costs: Discovery of Adverse Drug Reactions Through Large-Scale Analysis of Web Search Queries

      research-article
      , PhD 1 , , , PhD 1
      (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications Inc.
      machine learning, information retrieval, side effects, infoveillance, infodemiology

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          Abstract

          Background

          Postmarket drug safety surveillance largely depends on spontaneous reports by patients and health care providers; hence, less common adverse drug reactions—especially those caused by long-term exposure, multidrug treatments, or those specific to special populations—often elude discovery.

          Objective

          Here we propose a low cost, fully automated method for continuous monitoring of adverse drug reactions in single drugs and in combinations thereof, and demonstrate the discovery of heretofore-unknown ones.

          Methods

          We used aggregated search data of large populations of Internet users to extract information related to drugs and adverse reactions to them, and correlated these data over time. We further extended our method to identify adverse reactions to combinations of drugs.

          Results

          We validated our method by showing high correlations of our findings with known adverse drug reactions (ADRs). However, although acute early-onset drug reactions are more likely to be reported to regulatory agencies, we show that less acute later-onset ones are better captured in Web search queries.

          Conclusions

          Our method is advantageous in identifying previously unknown adverse drug reactions. These ADRs should be considered as candidates for further scrutiny by medical regulatory authorities, for example, through phase 4 trials.

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          Most cited references43

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          World Health Organization.

          Ala Alwan (2007)
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            Statistical comparison of classifiers over multiple data sets

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              Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership.

              The U.S. Food and Drug Administration (FDA) Amendments Act of 2007 mandated that the FDA develop a system for using automated health care data to identify risks of marketed drugs and other medical products. The Observational Medical Outcomes Partnership is a public-private partnership among the FDA, academia, data owners, and the pharmaceutical industry that is responding to the need to advance the science of active medical product safety surveillance by using existing observational databases. The Observational Medical Outcomes Partnership's transparent, open innovation approach is designed to systematically and empirically study critical governance, data resource, and methodological issues and their interrelationships in establishing a viable national program of active drug safety surveillance by using observational data. This article describes the governance structure, data-access model, methods-testing approach, and technology development of this effort, as well as the work that has been initiated.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications Inc. (Toronto, Canada )
                1439-4456
                1438-8871
                June 2013
                18 June 2013
                : 15
                : 6
                : e124
                Affiliations
                [1] 1Yahoo Research New York, NYUnited States
                Author notes
                Corresponding Author: Elad Yom-Tov eladyt@ 123456yahoo.com
                Article
                v15i6e124
                10.2196/jmir.2614
                3713931
                23778053
                11a135a7-ccad-4ac9-a399-4fbfab4a26ea
                ©Elad Yom-Tov, Evgeniy Gabrilovich. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.06.2013.

                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, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 14 March 2013
                : 07 April 2013
                : 26 April 2013
                : 23 May 2013
                Categories
                Original Paper

                Medicine
                machine learning,information retrieval,side effects,infoveillance,infodemiology
                Medicine
                machine learning, information retrieval, side effects, infoveillance, infodemiology

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