{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T14:13:58Z","timestamp":1758636838878,"version":"3.41.2"},"reference-count":37,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2017,8,14]],"date-time":"2017-08-14T00:00:00Z","timestamp":1502668800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["OIR"],"published-print":{"date-parts":[[2017,8,14]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to propose a framework to detect adverse drug reactions (ADRs) using internet user search data, so that ADR events can be identified early. Empirical investigation of Avandia, a type II diabetes treatment, is conducted to illustrate how to implement the proposed framework.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Typical ADR identification measures and time series processing techniques are used in the proposed framework. Google Trends Data are employed to represent user searches. The baseline model is a disproportionality analysis using official drug reaction reporting data from the US Food and Drug Administration\u2019s Adverse Event Reporting System.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Results show that Google Trends series of Avandia side effects search reveal a significant early warning signal for the side effect emergence of Avandia. The proposed approach of using user search data to detect ADRs is proved to have a longer leading time than traditional drug reaction discovery methods. Three more drugs with known adverse reactions are investigated using the selected approach, and two are successfully identified.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>Validation of Google Trends data\u2019s representativeness of user search is yet to be explored. In future research, user search in other search engines and in healthcare web forums can be incorporated to obtain a more comprehensive ADR early warning mechanism.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>Using internet data in drug safety management with a proper early warning mechanism may serve as an earlier signal than traditional drug adverse reaction. This has great potential in public health emergency management.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The research work proposes a novel framework of using user search data in ADR identification. User search is a voluntary drug adverse reaction exploration behavior. Furthermore, user search data series are more concise and accurate than text mining in forums. The proposed methods as well as the empirical results will shed some light on incorporating user search data as a new source in pharmacovigilance.<\/jats:p><\/jats:sec>","DOI":"10.1108\/oir-10-2015-0341","type":"journal-article","created":{"date-parts":[[2017,7,10]],"date-time":"2017-07-10T07:11:33Z","timestamp":1499670693000},"page":"524-536","source":"Crossref","is-referenced-by-count":3,"title":["Adverse drug reaction early warning using user search data"],"prefix":"10.1108","volume":"41","author":[{"given":"Wei","family":"Shang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hsinchun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christine","family":"Livoti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"11","key":"key2020120604111419400_ref001","doi-asserted-by":"crossref","first-page":"981","DOI":"10.2165\/00002018-200528110-00002","article-title":"Perspectives on the use of data mining in pharmacovigilance","volume":"28","year":"2005","journal-title":"Drug Safety"},{"issue":"7","key":"key2020120604111419400_ref002","first-page":"1","article-title":"Social network sites as a mode to collect health data: a systematic review","volume":"16","year":"2014","journal-title":"Journal of Medical Internet Research"},{"issue":"3","key":"key2020120604111419400_ref003","first-page":"288","article-title":"A novel evaluation of world no tobacco day in Latin America","volume":"14","year":"2012","journal-title":"Journal of Medical Internet Research"},{"issue":"4","key":"key2020120604111419400_ref004","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s002280050466","article-title":"A Bayesian neural network method for adverse drug reaction signal generation","volume":"54","year":"1998","journal-title":"European Journal of Clinical Pharmacology"},{"issue":"3","key":"key2020120604111419400_ref005","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.socscimed.2010.03.042","article-title":"A rapid method for assessing social versus independent interest in health issues: a case study of \u2018bird flu\u2019 and \u2018swine flu\u2019","volume":"71","year":"2010","journal-title":"Social Science & Medicine"},{"issue":"2","key":"key2020120604111419400_ref201","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.urology.2011.01.010","article-title":"Use of google insights for search to track seasonal and geographic kidney stone incidence in the united states","volume":"78","year":"2011","journal-title":"Urology"},{"issue":"7358","key":"key2020120604111419400_ref006","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1136\/bmj.325.7358.243","article-title":"Assessment of independent effect of olanzapine and risperidone on risk of diabetes among patients with schizophrenia: population based nested case-control study","volume":"325","year":"2002","journal-title":"British Medical Journal"},{"issue":"10","key":"key2020120604111419400_ref007","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1086\/630200","article-title":"Google Trends: a web-based tool for real-time surveillance of disease outbreaks","volume":"49","year":"2009","journal-title":"Clinical Infectious Diseases"},{"first-page":"217","article-title":"Predicting adverse drug events from personal health messages","year":"2011","key":"key2020120604111419400_ref008"},{"key":"key2020120604111419400_ref009","unstructured":"ComScore (2009), \u201ccomScore releases November US search engine rankings\u201d, available at: www.comscore.com\/Insights\/Press-Releases\/2009\/12\/comScore-Releases-November-2009-U.S.-Search-Engine-Rankings?cs_edgescape_cc=HK (accessed July 25, 2016)."},{"key":"key2020120604111419400_ref010","unstructured":"Cooper, S. 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