Cédric Bousquet

Implementation of automated signal generation in pharmacovigilance using a knowledge-based approach

Automated signal generation is a growing field in pharmacovigilance that relies on data mining of huge spontaneous reporting systems for detecting unknown adverse drug reactions (ADR). Previous implementations of quantitative techniques did not take into account issues related to the medical dictionary for regulatory activities (MedDRA) terminology used for coding ADRs. MedDRA is a first generation terminology lacking formal definitions; grouping of similar medical conditions is not accurate due to taxonomic limitations. Our objective was to build a data-mining tool that improves signal detection algorithms by performing terminological reasoning on MedDRA codes described with the DAML+OIL description logic. We propose the PharmaMiner tool that implements quantitative techniques based on underlying statistical and bayesian models. It is a JAVA application displaying results in tabular format and performing terminological reasoning with the Racer inference engine. The mean frequency of drug-adverse effect associations in the French database was 2.66. Subsumption reasoning based on MedDRA taxonomical hierarchy produced a mean number of occurrence of 2.92 versus 3.63 (p < 0.001) obtained with a combined technique using subsumption and approximate matching reasoning based on the ontological structure. Semantic integration of terminological systems with data mining methods is a promising technique for improving machine learning in medical databases.

Automatic creation and refinement of the clusters of pharmacovigilance terms

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs or biologics. The detection of adverse drug reactions is performed thanks to statistical algorithms and to groupings of ADR terms. Standardized MedDRA Queries (SMQs) are the groupings which become a standard for assisting the retrieval and evaluation of MedDRA-coded ADR reports all through the world. Currently 84 SMQs have been created manually by experts, while several important safety topics are not yet covered. Dependent on the context of their application, these SMQs show a high degree of sensitivity and often appear to be over-inclusive. For pharmacovigilance experts it represents an important and tedious filtering of data. The objective of this work is to propose an automatic method for assisting the creation of SMQs and also for the refinement of their organization further to the creation of smaller clusters of ADR terms. In this work we propose to exploit the semantic distance and clustering approaches. We perform several experiments and vary several parameters of the method.

Mise en oeuvre d’un processus de conception objet pour l’implémentation de règles de décisions destinées à la validation pharmaceutique

The most important part of alarm systems is the rules set that allows triggering alerts. We propose a generic rule design framework which can support the design and the implementation of alerts for drug prescriptions or their pharmaceutical validation. Our approach takes into account two important criteria, which represent two real challenges for the system designers. First, the developer needs to identify and model both users’ requirements and business processes in which the alarm system works. Second, the developer needs to model the knowledge associated with the decision rules with an appropriate language. We show how the Unified Process helps to model the business process for pharmaceutical validation. Knowledge representation benefited from use of SBVR, a language dedicated to rule representation within an object oriented framework. After a successful implementation of a prototype we plan to integrate such system within the hospital information system of the Georges Pompidou hospital.

Définition d’une structure catégorielle pour les effets indésirables liés aux médicaments

The CEN Categorial structure is defined as a minimal set of health care domain constraints to represent a biomedical terminology in a precise healthcare domain. We present in this paper the first proposal of a categorial structure within pharmacovigilance. This study is based on the analysis and definition of 639 WHO-ART terms and the construction of an ontological model in an iterative way. Starting from a basic model based on previous experience, we added new semantic categories. changed the relations until the schema allowed to represent every term in a consistent way. We plan to investigate how this lite ontological model can help for better definition of adverse drug reactions using knowledge engineering tools such as ontology editors and reasoners.