Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network

作者：

Highlights：

• Dynamic Bayesian Network (DBN) built using a lung cancer staging state-space model.

• Use of resampling techniques to address data imbalance.

• Results are comparable to experts’ decisions.

• Similar structure and performance between learned and expert-derived DBNs.

摘要

Highlights•Dynamic Bayesian Network (DBN) built using a lung cancer staging state-space model.•Use of resampling techniques to address data imbalance.•Results are comparable to experts’ decisions.•Similar structure and performance between learned and expert-derived DBNs.

论文关键词：Dynamic Bayesian networks,Structure learning,Expert-driven networks,Lung stage cancer state-space,Individualized lung cancer screening,Cancer incidence,Annual NLST cancer risk

论文评审过程：Received 24 March 2016, Accepted 25 July 2016, Available online 27 July 2016, Version of Record 2 September 2016.

论文官网地址：https://doi.org/10.1016/j.artmed.2016.07.001