Non-parametric kernel density estimation for the prediction of neoadjuvant chemotherapy outcomes

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 eac...

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Bibliographic Details
Published in:2010 Annual International Conference of the IEEE Engineering in Medicine and Biology 2010-01, p.1775-1778
Main Authors: Wanderley, M F B, Braga, Antônio P, Mendes, E M A M, Natowicz, René, Rouzier, R
Format: Article
Language:English
Subjects:
Bayesian methods
Breast cancer
Data models
Kernel
Logistics
Probes
Online Access:Request full text
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Summary: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.
ISSN:1094-687X
1558-4615
DOI:10.1109/IEMBS.2010.5626748