A Dominance-Based Rough Set Approach for an Enhanced Assessment of Seasonal Influenza Risk.

Accounting for about 290,000-650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision-making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S-OLAP) technology. Although the S-OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well-known multicriteria classification method, the dominance-based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S-OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.

The Information Quality Triangle: a methodology to assess clinical information quality.

Building qualitative clinical decision support or monitoring based on information stored in clinical information (or EHR) systems cannot be done without assessing and controlling information quality. Numerous works have introduced methods and measures to qualify and enhance data, information models and terminologies quality. This paper introduces an approach based on an Information Quality Triangle that aims at providing a generic framework to help in characterizing quality measures and methods in the context of the integration of EHR data in a clinical datawarehouse. We have successfully experimented the proposed approach at the HEGP hospital in France, as part of the DebugIT EU FP7 project.