微阵列和临床数据的顺序分类

G. Tusch
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引用次数: 5

摘要

顺序分类在逐步过程中只使用部分数据(证据)进行部分分类,即只对具有足够证据的对象进行分类,其余对象不进行分类。在接下来的步骤中,使用其他数据重复该过程,直到所有对象都被分类。当数据仅在特定时间点可用时,如在手术决策中,即临床患者数据、实验室数据或组织样本的cDNA微阵列表达数据在手术前、手术中和手术后可用时,这尤其有用。外科医生有兴趣将患者分为低风险组和高风险组,这可能需要特殊措施,例如延长重症监护时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sequential classification for microarray and clinical data
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.
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