Deep reinforcement learning for personalized treatment recommendation.

ABSTRACT: In precision medicine, the ultimate goal is to recommend the most effective treatment to an individual patient based on patient-specific molecular and clinical profiles, possibly high-dimensional. To advance cancer treatment, large-scale screenings of cancer cell lines against chemical compounds have been performed to help better understand the relationship between genomic features and drug response; existing machine learning approaches use exclusively supervised learning, including penalized regression and recommender systems. However, it would be more efficient to apply reinforcement learning to sequentially learn as data accrue, including selecting the most promising therapy for a patient given individual molecular and clinical features and then collecting and learning from the correspo

SUBMITTER: Liu M 

PROVIDER: S-EPMC9427729 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature