Journal
IEEE ACCESS
Volume 7, Issue -, Pages 119403-119419Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2935763
Keywords
Early disease prediction; dynamic Bayesian networks; lung cancer screening; partially observable Markov decision processes; QMDP algorithm
Categories
Funding
- NIH/NCI [R01 CA226079, R01 CA210360]
- NSF [1722516]
- UCLA Radiological Sciences Data Driven Diagnostic Decision Support (D4S) Program
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Globally, lung cancer is responsible for nearly one in five cancer deaths. The National Lung Screening Trial (NLST) demonstrated the efficacy of low-dose computed tomography (LDCT) to identify early-stage disease, setting the basis for widespread implementation of lung cancer screening programs. However, the specificity of LDCT lung cancer screening is suboptimal, with a significant false positive rate. Representing this imaging-based screening process as a sequential decision making problem, we combined multiple machine learning-based methods to learn a partially-observable Markov decision process that simultaneously optimizes lung cancer detection while enhancing test specificity. Using NLST data, we trained a dynamic Bayesian network as an observational model and used inverse reinforcement learning to discover a rewards function based on experts' decisions. Our resultant predictive model decreased the false positive rate while maintaining a high true positive rate at a level comparable to human experts. Our model also detected a number of lung cancers earlier.
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