Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
Local Dimensionality Reduction within Natural Clusters for Medical Data Analysis
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Dynamic integration of classifiers for handling concept drift
Information Fusion
MACMESE'10 Proceedings of the 12th WSEAS international conference on Mathematical and computational methods in science and engineering
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Nosocomial infections and antimicrobial resistance (AR) are highly important problems that impact the morbidity and mortality of hospitalized patients as well as their cost of care. The goal of this paper is to demonstrate our analysis of AR by applying a number of various data mining (DM) techniques to real hospital data. The data for the analysis includes instances of sensitivity of nosocomial infections to antibiotics collected in a hospital over three years 2002-2004. The results of our study show that DM makes it easy for experts to inspect patterns that might otherwise be missed by usual (manual) infection control. However, the clinical relevance and utility of these findings await the results of prospective studies. We see our main contribution in this paper in introducing and applying a many-sided analysis approach to real-world data. The application of diversified DM techniques, which are not necessarily accurate and do not best suit to the present problem in the usual sense, still offers a possibility to analyze and understand the problem from different perspectives.