Dynamic Treatment Strategies via Q-Learning and Deep Learning-Based Buckley-James Method

Music City Center 

In healthcare, developing personalized treatment strategies is essential for optimizing patient outcomes, particularly when dealing with censored survival data. This study introduces the Dynamic Deep Buckley-James Q-Learning algorithm, a novel methodology that integrates reinforcement learning with the Buckley-James method to manage censored data effectively. By leveraging deep learning techniques, the algorithm enhances the predictive accuracy of survival times in complex, non-linear settings, optimizing treatment decisions based on imputed outcomes. Our comprehensive simulation study, which includes scenarios with missing at random (MAR), not missing at random (NMAR) data, and right-censoring, demonstrates the algorithm's robust performance. The ability to handle various types of missing and censored data ensures wide applicability across different clinical contexts. By addressing the complexities and challenges associated with censoring and missing data in survival analysis, the algorithm learns policies that maximize the expected total imputed survival reward for patients. This enables the comparison of imputed survival times across different treatments, a feature not possible

Deep Learning

Q-Learning

Imputation

Dynamic Treatment Regime 

Korean International Statistical Society