Adverse drug effects endanger patients’ safety and cause considerable extra healthcare costs. The procedures of drug surveillance and drug harm prevention are studied by pharmacovigilance. Several Information Technology (IT) methods are currently employed for conducting research as regards identification of pharmacovigilance signals. The current project (SAFER) will elaborate on exploiting and complementing evidence obtained from various types of existing pharmacovigilance signal sources through: (a) the semantic harmonization and mediation among the respective signal detection methods, and (b) the development of signal aggregation and reasoning mechanisms that will enable knowledge fusion of the obtained outcomes, introducing overall an integrated, semantic approach for obtaining and managing knowledge on pharmacovigilance signals. The proposed approach is particularly applicable, since signal generation is characterized by incomplete knowledge and uncertainty in the results obtained from all types of signal detection methods, thus, a synthesis of all possible signal sources is necessary. SAFER aims to properly detect true associations and minimize false positive findings, which are the major drawbacks of most signal detectors currently available, so as to reinforce the reliability and completeness of pharmacovigilance signals. Major expected results of SAFER constitute: (a) a generic knowledge model that will address the heterogeneity of the available signal detectors via their semantic harmonization, (b) an advanced decision support service for medication error prevention based on the introduced reasoning scheme, and (c) a semantically-enriched IT framework for integrated pharmacovigilance research. SAFER will be conducted in the INSERM - U872, Eq20 - group, a centre of excellence in the field of knowledge engineering and semantic technologies in e-health with major application domain pharmacovigilance.
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