TY - GEN
T1 - Improved Design of Bayesian Networks for Modelling Toxicity Risk in Breast Radiotherapy using Dynamic Discretization
AU - Ciunkiewicz, Philip
AU - Yanushkevich, Svetlana
AU - Roumeliotis, Michael
AU - Stenhouse, Kailyn
AU - McGeachy, Philip
AU - Quirk, Sarah
AU - Grendarova, Petra
N1 - Funding Information:
This work was partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Strategic Partnership Grant; and by the NSERC Canada Graduate Scholarships - Master’s program.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This study investigates the dynamic discretization approach with the purpose of improving probabilistic causal models for the task of toxicity risk assessment in breast radiother-apy. We considered a probabilistic causal model such as Bayesian Networks, and implemented a modified version of Fenton & Neil's dynamic discretization algorithm to validate and analyze these models in terms of performance. Our implementation performs discretization at the data- or distribution-level. This approach is shown to provide significant improvement over static methods when assessing distribution fit via relative entropy error, as well as being very computationally efficient. Predictive performance was compared across four distinct datasets using Bayesian Networks, and dynamic discretization was not found to consistently outperform static techniques despite generating discretizations with significantly better fit to their underlying distributions.
AB - This study investigates the dynamic discretization approach with the purpose of improving probabilistic causal models for the task of toxicity risk assessment in breast radiother-apy. We considered a probabilistic causal model such as Bayesian Networks, and implemented a modified version of Fenton & Neil's dynamic discretization algorithm to validate and analyze these models in terms of performance. Our implementation performs discretization at the data- or distribution-level. This approach is shown to provide significant improvement over static methods when assessing distribution fit via relative entropy error, as well as being very computationally efficient. Predictive performance was compared across four distinct datasets using Bayesian Networks, and dynamic discretization was not found to consistently outperform static techniques despite generating discretizations with significantly better fit to their underlying distributions.
KW - Bayesian network
KW - discretization
KW - information theory
KW - machine reasoning
KW - probability distribution
UR - http://www.scopus.com/inward/record.url?scp=85140739492&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85140739492&partnerID=8YFLogxK
U2 - 10.1109/IJCNN55064.2022.9892531
DO - 10.1109/IJCNN55064.2022.9892531
M3 - Conference contribution
AN - SCOPUS:85140739492
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Joint Conference on Neural Networks, IJCNN 2022
Y2 - 18 July 2022 through 23 July 2022
ER -