title: Non-parametric kernel density estimation for the prediction of neoadjuvant chemotherapy outcomes.
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.
authors.affiliation: Departamento de Engenharia Eletrônica, Universidade Federal de Minas Gerais, Brazil. mfbw@cpdee.ufng.br
authors.forename: Maria Fernanda B
authors.initials: MF
authors.lastname: Wanderley
authors.forename: Antonio P
authors.initials: AP
authors.lastname: Braga
authors.forename: Eduardo M A M
authors.initials: EM
authors.lastname: Mendes
authors.forename: Rene
authors.initials: R
authors.lastname: Natowicz
authors.forename: Roman
authors.initials: R
authors.lastname: Rouzier
mesh_terms: D000465:Algorithms; D001938:Brazil / Q000453:epidemiology; D001943:Breast Neoplasms / Q000401:mortality* / Q000628:therapy*; D017024:Chemotherapy, Adjuvant / Q000401:mortality*; D020360:Neoadjuvant Therapy / Q000401:mortality*; D017063:Outcome Assessment, Health Care / Q000379:methods*; D015995:Prevalence; D011379:Prognosis; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D015996:Survival Rate