Patient level simulation and reinforcement learning to discover novel strategies for treating ovarian cancer
Year: 2021
Type: preprint
Abstract: The prognosis for patients with epithelial ovarian cancer remains dismal despite improvements in survival for other cancers. Treatment involves multiple lines of chemotherapy and becomes increasingly heterogeneous after first-line therapy. Reinforcement learning with real-world outcomes data has the potential to identify novel treatment strategies to improve overall survival. We design a reinforcement learning environment to model epithelial ovarian cancer treatment trajectories and use model free reinforcement learning to investigate therapeutic regimens for simulated patients.
Source: arXiv (Cornell University)
Authors: Murphy, Brian, Nasir-Moin, Mustafa, von Oiste, Grace, Chen, Viola, Riina, Howard A, Kondziolka, Douglas, Oermann, Eric K
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3210302619