UCLA

Currently, low-dose computed tomography (LDCT) is the only recommended screening test for patients who are at high risk of lung cancer. However, the cost-benefit analysis of LDCT must be weighed against the high number of false positives, radiation exposure, unnecessary procedures, and the associated patient distress as a result of the aforementioned possibilities. Sequential decision making models such as the partially observable Markov decision process (POMDP) have seen success in making recommendations in clinical applications such as lung cancer screening. Enabled by the availability of longitudinal datasets that track patient health over time, these models make predictions toward long-term health outcomes. A key challenge in lung cancer screening is the balancing between true positives and false positives, that is, maximizing true positives while minimizing false positives. This dissertation attempts to address this challenge by leveraging a variety of techniques toward optimizing decision making over time. First, the modularized POMDP (modPOMDP) framework is developed to account for temporal variations within a POMDP model. Each time point is optimized separately to ensure "earlier" detection by maximizing positive predictions over the entire screening duration. Second, a two-part model framework (modPOMDP2) is developed to differentiate true and false positive predictions from each other. This method combines classic machine learning (ML) techniques and modPOMDP to maintain true positives while decreasing false positives. Third, the validity of these approaches is demonstrated in an external testing dataset.