Predictions in antibiotics resistance and nosocomial infections monitoring

Nosocomial infections and antibiotic resistance are regarded as critical issues both in clinical medicine as well as in Public health, thus understanding their epidemiology is a priority in the health sector. Our research aims at demonstrating that data mining techniques, such as regression, classification and association rules and assist in discovering interesting patterns in the epidemiological trends of antibiotic resistance in Greek Hospitals. In this work, we present a novel framework which integrates data from multiple hospitals and discovers association rules stored in a data warehouse. Furthermore, this data warehouse is used as a source for extracting interesting and valid predictions by applying techniques such as regression and classification. Our system is fully operational and treats real-world data from the WHONET, a software installed on the majority of Greek member hospitals of the "Greek System for Surveillance of Antimicrobial Resistance" network. The contributions of the proposed framework are i. a standardized workflow for the seamless integration of data produced in various hospitals into a consistent data warehouse and b. the use of a mechanisms to predict hidden future behavior on large datasets, using regression and classification algorithms, which can provide significant surveillance warnings.