Multi-Objective Deep Reinforcement Learning for Personalized Dose Optimization Based on Multi-Indicator Experience Replay

Abstract

Chemotherapy as an effective method is now widely used to treat various types of malignant tumors. With advances in medicine and drug dosimetry, the precise dose adjustment of chemotherapy drugs has become a significant challenge. Several academics have investigated this problem in depth. However, these studies have concentrated on the efficiency of cancer treatment while ignoring other significant bodily indicators in the patient, which could cause other complications. Therefore, to handle the above problem, this research creatively proposes a multi-objective deep reinforcement learning. First, in order to balance the competing indications inside the optimization process and to give each indicator a better outcome, we propose a multi-criteria decision-making strategy based on the integration concept. In addition, we provide a novel multi-indicator experience replay for multi-objective deep reinforcement learning, which significantly speeds up learning compared to conventional approaches. By modeling various indications in the body of the patient, our approach is used to simulate the treatment of tumors. The experimental results demonstrate that the treatment plan generated by our method can better balance the contradiction between the tumor’s treatment effect and other biochemical indicators than other treatment plans, and its treatment time is only one-third that of multi-objective deep reinforcement learning, which is now in use.