A4 Article in conference proceedings
Knowledge Discovery from Microbiology Data : Many-sided Analysis of Antibiotic Resistance in Nosocomial Infections (2005)
Pechenizkiy, M., Tsymbal, A., Puuronen, S., Shifrin, M., & Alexandrova, I. (2005). Knowledge Discovery from Microbiology Data : Many-sided Analysis of Antibiotic Resistance in Nosocomial Infections. In Professional Knowledge Management (pp. 360-372). Springer Verlag. Lecture Notes in Computer Science, 3782. https://doi.org/10.1007/11590019_41
JYU authors or editors
Publication details
All authors or editors: Pechenizkiy, Mykola; Tsymbal, Alexey; Puuronen, Seppo; Shifrin, Mishael; Alexandrova, Irina
Parent publication: Professional Knowledge Management
Place and date of conference: Kaiserslautern, Germany , 10.-13.4.2005
Journal or series: Lecture Notes in Computer Science
ISSN: 0302-9743
eISSN: 1611-3349
Publication year: 2005
Number in series: 3782
Pages range: 360-372
Publisher: Springer Verlag
Publication country: Germany
Publication language: English
DOI: https://doi.org/10.1007/11590019_41
Publication open access: Not open
Publication channel open access:
Abstract
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.
Keywords: data mining; patient information systems; nosocomial infection; drug resistance
Free keywords: data mining; patient information systems; nosocomial infection; drug resistance
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Ministry reporting: Yes
Preliminary JUFO rating: Not rated
