Simulation-based optimization of radiotherapy: Agent-based modeling and reinforcement learning

By A. Jalalimanesh, H. Haghighi, Abbas Ahmadi, M. Soltani. 2017

78 citations recorded by the source corpus.

Subfields: Cancer Research, Modeling and Simulation, Radiation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

In Modeling and Simulation

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

In Radiation

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “gy. Tseng et al. [93] Q-learning N/A DRL (DQN) Data from 114 NSCLC patients Addressing limited sample size problem using GAN and approximating the transition probability using DNN. Jalalimanesh et al.[94] Q-learning N/A N/A Agent-based model Using agent-based simulation to model the dynamics of tumo”
• Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis (2018, methodology): “The authors employ tabular Q-learning [26] to learn the optimal policy of radiotherapy, using an environment comprising an underlying ABM of vascular tumor growth.”
• Multi-objective optimization of radiotherapy: distributed Q-learning and agent-based simulation (2017, methodology): “In previous work, we considered RT as a single-objective problem and proposed an optimization methodology by the aid of agent-based simulation and Q-learning (Jalalimanesh et al., 2017).”