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Data-Mining in klinischen Datensätzen: Bericht der Arbeitsgruppe Bioinformatik der DGKL

Data mining programs help with extracting knowledge from large amounts of data. Despite decades of experience with profile testing, laboratory medicine is just now realizing the practical application of these programs to highly parallel analytical techniques (genomics, proteomics, etc.). Professiona...

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Bibliographic Details
Main Authors: Hoffmann, Georg (Author) , Zapatka, Marc (Author) , Findeisen, Peter (Author) , Wörner, Stefan M. (Author) , Martus, Peter (Author) , Neumaier, Michael (Author)
Format: Article (Journal)
Language:German
Published: 22. Juli 2010
In: Laboratoriumsmedizin
Year: 2010, Volume: 34, Issue: 4, Pages: 227-233
ISSN:1439-0477
DOI:10.1515/jlm.2010.041
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1515/jlm.2010.041
Verlag, lizenzpflichtig, Volltext: https://www.degruyterbrill.com/document/doi/10.1515/jlm.2010.041/html
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Author Notes:Georg Hoffmann, Marc Zapatka, Peter Findeisen, Stefan Wörner, Peter Martus, Michael Neumaier
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Summary:Data mining programs help with extracting knowledge from large amounts of data. Despite decades of experience with profile testing, laboratory medicine is just now realizing the practical application of these programs to highly parallel analytical techniques (genomics, proteomics, etc.). Professional data preparation, and most specifically data normalization is crucial for the success of any data mining project. Using routine hospital admission data, we demonstrate how explorative cluster analysis can identify meaningful result patterns. Based upon this feasibility study, the German Association for Clinical Chemistry and Laboratory Medicine is now supporting a research and software development project.
Item Description:Gesehen am 06.03.2023
Physical Description:Online Resource
ISSN:1439-0477
DOI:10.1515/jlm.2010.041

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