• DocumentCode
    2526537
  • Title

    Sequential classification for microarray and clinical data

  • Author

    Tusch, Guenter

  • Author_Institution
    Grand Valley State Univ., Allendale, MI, USA
  • fYear
    2005
  • fDate
    8-11 Aug. 2005
  • Firstpage
    5
  • Lastpage
    6
  • Abstract
    Sequential classification uses in a stepwise process only part of the data (evidence) for partial classification, i.e., classifying only objects with sufficient evidence and leaving the rest unclassified. In the following steps the procedure is repeated using additional data until all objects are classified. This is especially useful when data become available only at certain points in time, as in surgical decision making, i.e., clinical patient data, lab data, or cDNA microarray expression data from tissue samples become available before, during and after the operation. Surgeons are interested in classifying patients into low or high risk groups, which might need special measures, e.g., prolonged intensive care.
  • Keywords
    DNA; decision making; medical computing; patient care; pattern classification; surgery; cDNA microarray expression data; clinical patient data; lab data; object classification; prolonged intensive care; sequential classification; surgical decision making; tissue samples; Artificial neural networks; Bioinformatics; Clinical trials; Costs; Decision making; Error analysis; Neoplasms; Neurons; Statistical analysis; Surgery;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Systems Bioinformatics Conference, 2005. Workshops and Poster Abstracts. IEEE
  • Print_ISBN
    0-7695-2442-7
  • Type

    conf

  • DOI
    10.1109/CSBW.2005.123
  • Filename
    1540518