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Abstract

In the present study, antibiotic resistance data generated in Greece by the WHONET Network were further analyzed by the use of data mining techniques. More specifically association rules were extracted among data collected in the Microbiology Dept. of “Sismanoglion ” General Hospital, a 500-bed general hospital, in Athens, Greece. The data studied were the susceptibility results, as well as data concerning the patient’s wards, the day of isolation and the type of clinical specimen, of a total of 20,794 bacterial isolates collected in the period January 1 st 1996 to December 31 st 2000,. The factors used to measure the importance of each association rule were its strength (confidence), its support, its coverage, its leverage and its lift. Two main rule categories were generated, one associating clinical specimen, time and ward of isolation, with bacterial species and the second one associating the same attributes with resistant phenotypes. The factors most often used to compare and evaluate different rules were leverage and lift. The system generated association rules in an unsupervised automatic way and revealed pieces of knowledge not easily available with standard supervised procedures of analysis, thus making it very useful in an automated public health surveillance system.