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2015 | OriginalPaper | Chapter

Patient-Specific Modeling of Medical Data

Authors : Guilherme Alberto Sousa Ribeiro, Alexandre Cesar Muniz de Oliveira, Antonio Luiz S. Ferreira, Shyam Visweswaran, Gregory F. Cooper

Published in: Machine Learning and Data Mining in Pattern Recognition

Publisher: Springer International Publishing

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Abstract

Patient-specific models are instance-based learn algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the standard entropy-based method, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.

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Metadata
Title
Patient-Specific Modeling of Medical Data
Authors
Guilherme Alberto Sousa Ribeiro
Alexandre Cesar Muniz de Oliveira
Antonio Luiz S. Ferreira
Shyam Visweswaran
Gregory F. Cooper
Copyright Year
2015
Book
Machine Learning and Data Mining in Pattern Recognition

Print ISBN: 978-3-319-21023-0

Electronic ISBN: 978-3-319-21024-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-21024-7_29

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