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

Author

Wanderley, Maria Fernanda B ; Braga, Ant Onio P ; Mendes, Eduardo M A M ; Natowicz, René ; Rouzier, Roman

Author_Institution

Dept. de Eng. Eletron., Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil

fYear

2010

fDate

Aug. 31 2010-Sept. 4 2010

Firstpage

1775

Lastpage

1778

Abstract

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.

Keywords

Bayes methods; cancer; drugs; regression analysis; statistical analysis; tumours; Bayesian classifiers; local statistical models; logistic regression; neoadjuvant chemotherapy; nonparametric kernel density estimation; pathologic complete response; Bayesian methods; Breast cancer; Data models; Kernel; Logistics; Probes; Algorithms; Brazil; Breast Neoplasms; Chemotherapy, Adjuvant; Neoadjuvant Therapy; Outcome Assessment (Health Care); Prevalence; Prognosis; Proportional Hazards Models; Reproducibility of Results; Risk Assessment; Risk Factors; Sensitivity and Specificity; Survival Analysis; Survival Rate;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE

Conference_Location

Buenos Aires

ISSN

1557-170X

Print_ISBN

978-1-4244-4123-5

Type

conf

DOI

10.1109/IEMBS.2010.5626748

Filename

5626748