Mining Safety Signals in Spontaneous Report Database using Concept Analysis

Amine Mohamed Rouane Hacene 1 Yannick Toussaint 1 Petko Valtchev 2
1 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In pharmacovigilance, linking the adverse reactions by patients to drugs they took is a key activity typically based on the analysis of patient reports. Yet generating potentially interesting pairs (drug, reaction) from a record database is a complex task, especially when many drugs are involved. To limit the generation effort, we exploit the frequently occurring patterns in the database and form \textit{association rules} on top of them. Moreover, only rules of minimal premise are considered as output by concept analysis tools, which are then filtered through standard measures for statistical significance. We illustrate the process on a small database of anti-HIV drugs involved in the HAART therapy while larger-scope validation within the database of the French Medicines Agency is also reported.
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Carlo Combi, Yuval Shahar and Ameen Abu-Hanna. 12th Conference on Artificial Intelligence in Medicine - AIME 2009, Jul 2009, Verona, Italy. Springer Berlin / Heidelberg, 5651, pp.285-294, 2009, Lecture Notes in Computer Science. 〈http://www.springerlink.com/content/15u5522417802824/〉. 〈10.1007/978-3-642-02976-9_41〉
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Amine Mohamed Rouane Hacene, Yannick Toussaint, Petko Valtchev. Mining Safety Signals in Spontaneous Report Database using Concept Analysis. Carlo Combi, Yuval Shahar and Ameen Abu-Hanna. 12th Conference on Artificial Intelligence in Medicine - AIME 2009, Jul 2009, Verona, Italy. Springer Berlin / Heidelberg, 5651, pp.285-294, 2009, Lecture Notes in Computer Science. 〈http://www.springerlink.com/content/15u5522417802824/〉. 〈10.1007/978-3-642-02976-9_41〉. 〈inria-00437224〉

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