Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy.

ABSTRACT: Subtle differences in a patient's genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can estimate an individual patient's dose response mid-treatment and recommend an optimal dose adjustment. Our framework considers patients' specific information including biological, physical, genetic, clinical, and dosimetric factors. Recognizing that physicians must make decisions amidst uncertainty in RT treatment outcomes, we employed indeterministic quantum states to represent human decision making in a real-life scenario. We paired quantum decision states with a model-based deep q-learning algo

SUBMITTER: Niraula D 

PROVIDER: S-EPMC8651664 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature