Privacy-preserving reinforcement learning design for patient-centric dynamic treatment regimes

• Main Authors: LIU, Ximeng, DENG, Robert H., CHOO, Kim-Kwang Raymond, YANG, Yang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Cryptography; Diseases; Dynamic Treatment Regime; Experience Replay; Patient-Centric; Privacy; Privacy-Preserving; Protocols; Q-learning; Reinforcement learning; Reinforcement Learning; Cloud computing; Computational modeling; Health Information Technology; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/5073
• Institution: Singapore Management University

In this paper, we propose a privacy-preserving reinforcement learning framework for a patient-centric dynamic treatment regime, which we refer to as Preyer. Using Preyer, a patient-centric treatment strategy can be made spontaneously while preserving the privacy of the patient's current health...