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
Link To Document :
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