Paper Info
Title
Bayesian Network Learning for Detecting Reliable Interactions of Dose-Volume Related Parameters in Radiation Pneumonitis
Abstract
Due to the high fatality rate of patients with radiation pneumonitis (RP), a complication of the radiation therapy (radiotherapy), great attention has been paid to the treatment plan of individual RP patients. Therefore, not only technological advances in the development of treatment planning systems but also new prognostic models are urgently required to lessen the complication and to predict the state of patients more accurately. The Bayesian network is a useful tool for finding interactions among features and for developing prognostic models that enable physician to predict the outcome of radiotherapy. In this paper, we show the interactions among dosimetric features through Bayesian network structures and the performance of Bayesian classifiers with different search algorithms on a non-small cell lung cancer (NSCLC) dataset.
Year
DOI
Venue
2009
10.1109/ICMLA.2009.122
ICMLA
Keywords
Field
DocType
radiation pneumonitis,radiation therapy,belief networks,radiation pneumonitis (rp),bayesian classifier,treatment planning system,new prognostic model,bayesian network structure,treatment planning systems,pattern classification,bayesian classifiers,dose-volume related parameters,detecting reliable interactions,nsclc,dosimetric features,nonsmall cell lung cancer dataset,patient fatality rate,dosimetry,cancer,dose volume related parameters,search algorithms,reliable interactions detection,bayesian network,bayesian network learning,individual rp patient,treatment plan,prognostic model,medical computing,prognostic models,entropy,treatment planning,search algorithm,bayesian methods,data mining
Lung cancer,Computer science,Radiation treatment planning,Dosimetry,Case fatality rate,Radiation therapy,Bayesian network,Artificial intelligence,Machine learning,Cancer,Bayesian probability
Conference
ISBN
Citations 
PageRank 
978-0-7695-3926-3
0
0.34
References 
Authors
11
2
Name
Order
Citations
PageRank
Jung Hun Oh16113.11
Issam El-Naqa252836.31