DocumentCode :
2931232
Title :
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
Link To Document :
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