用于预测新辅助化疗结果的非参数核密度估计
Non-parametric kernel density estimation for the prediction of neoadjuvant chemotherapy outcomes.
作者信息
Wanderley Maria Fernanda B, Braga Antonio P, Mendes Eduardo M A M, Natowicz Rene, Rouzier Roman
机构信息
Departamento de Engenharia Eletrônica, Universidade Federal de Minas Gerais, Brazil.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1775-8. doi: 10.1109/IEMBS.2010.5626748.
In this paper we propose an application of local statistical models to the problem of identifying patients with pathologic complete response (PCR) to neoadjuvant chemotherapy. The idea of using local models is to split the input space (with data from PCR and NoPCR patients) and build a model for each partition. After the construction of the models we used bayesian classifiers and logistic regression to classify patients in the two classes.
在本文中，我们提出将局部统计模型应用于识别新辅助化疗后病理完全缓解（PCR）患者的问题。使用局部模型的思路是划分输入空间（包含来自PCR患者和非PCR患者的数据），并为每个分区构建一个模型。在构建模型之后，我们使用贝叶斯分类器和逻辑回归对两类患者进行分类。
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