A large scale data mining approach to antibiotic resistance surveillance

One of the most considerable functions in a hospital's infection control program is the surveillance of antibiotic resistance. Several traditional methods used to measure it do not provide adequate and promising results for further analysis. Data mining techniques, such as the association rules, have been used in the past and successfully led to discovering interesting patterns in public health data. In this work, we present the architecture of a novel framework which integrates data from multiple hospitals, discovers association rules, stores them in a data warehouse for future analysis and provides anytime accessibility through an intuitive web interface. We implemented the proposed architecture as a web application and evaluated it using data from the WHONET software installed in many Greek hospitals that belong to "the Greek System for Surveillance of Antimicrobial Resistance" network. The contribution of the proposed framework is considered to be a standardized workflow aiming at the integration of data produced by various hospitals into a consistent data warehouse and the use of a mechanism that detects hidden and previously unknown patterns on large datasets, in terms of association rules, which can provide surveillance warnings.