Patterns embedded in large volumes of clinical data may provide important insights
into the characteristics of patients or care delivery processes, but may be difficult
to identify by traditional means. Data mining offers methods that can recognize patterns
in these large data sets and make them actionable. We present an example of this capability
in which we successfully applied data mining to hospital infection control. The Data
Mining Surveillance System (DMSS) uses data from the clinical laboratory and hospital
information systems to create association rules linking patients, sample types, locations,
organisms, and antibiotic susceptibilities. Changes in association strength over time
signal epidemiologic patterns potentially appropriate for follow-up, and additional
heuristic methods identify the most informative of these patterns for alerting.
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© 2008 Elsevier Inc. Published by Elsevier Inc. All rights reserved.
