Predicting adverse drug events from personal health messages.
作者： Brant W. Chee , Bruce R Schatz , Richard B Berlin
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摘要: Adverse drug events (ADEs) remain a large problem in the United States, being fourth leading cause of death, despite post market surveillance. Much consumer surveillance relies on self-reported “spontaneous” patient data. Previous work has performed datamining over FDA’s Event Reporting System (AERS) and other spontaneous reporting systems to identify interactions drugs correlated with high rates serious adverse events. However, safety problems have resulted from lack marketing information about drugs, underreporting up 98% within such systems1,2. We explore use online health forums as source data for further FDA scrutiny. In this we aggregate individuals’ opinions review similar crowd intelligence3. We natural language processing group discussed ways are able successfully withdrawn based messages discussing them before their removal.
elsevier.com参考文章(17)
Krystl Haerian, Herbert S Chase, Carol Friedman, Rave Harpaz, Statistical Mining of Potential Drug Interaction Adverse Effects in FDA’s Spontaneous Reporting System american medical informatics association annual symposium. ,vol. 2010, pp. 281- 285 ,(2010)
Donald C. Comeau, Lana Yeganova, Won Kim, W. John Wilbur, Text Mining Techniques for Leveraging Positively Labeled Data meeting of the association for computational linguistics. pp. 155- 163 ,(2011)
Carol Friedman, Discovering Novel Adverse Drug Events Using Natural Language Processing and Mining of the Electronic Health Record artificial intelligence in medicine in europe. pp. 1- 5 ,(2009) , 10.1007/978-3-642-02976-9_1
Brant W. Chee, Bruce R. Schatz, Richard Berlin, Measuring population health using personal health messages. american medical informatics association annual symposium. ,vol. 2009, pp. 92- 96 ,(2009)
A. Blenkinsopp, P. Wilkie, M. Wang, P. A. Routledge, Patient reporting of suspected adverse drug reactions: a review of published literature and international experience British Journal of Clinical Pharmacology. ,vol. 63, pp. 148- 156 ,(2007) , 10.1111/J.1365-2125.2006.02746.X
Mark McClellan, Drug safety reform at the FDA--pendulum swing or systematic improvement? The New England Journal of Medicine. ,vol. 356, pp. 1700- 1702 ,(2007) , 10.1056/NEJMP078057
Laura Wojtulewicz, Robert Leaman, Graciela Gonzalez, Ryan Sullivan, Annie Skariah, Jian Yang, Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks meeting of the association for computational linguistics. pp. 117- 125 ,(2010)
Alan M Hochberg, Stephanie J Reisinger, Ronald K Pearson, Donald J O’Hara, Kevin Hall, None, Using Data Mining to Predict Safety Actions from FDA Adverse Event Reporting System Data Drug Information Journal. ,vol. 41, pp. 633- 643 ,(2007) , 10.1177/009286150704100510
Michael Kuhn, Monica Campillos, Ivica Letunic, Lars Juhl Jensen, Peer Bork, A side effect resource to capture phenotypic effects of drugs Molecular Systems Biology. ,vol. 6, pp. 343- 343 ,(2010) , 10.1038/MSB.2009.98