Adversarial reinforcement learning for dynamic treatment regimes.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Healthcare Informatics

Background:

• Treatment recommendation is crucial for precision medicine and healthcare management.
• Reinforcement learning (RL) offers a promising approach for optimizing treatment policies.
• Current RL methods often require extensive and costly clinical interactions, limiting their practical application.

Purpose of the Study:

• To develop a model-based offline reinforcement learning approach for treatment recommendation.
• To leverage Electronic Health Records (EHRs) for training treatment policies without direct clinical interaction.
• To address the limitations of existing RL methods in clinical settings.

Main Methods:

• Constructed a patient treatment trajectory simulator using ground-truth EHR data.
• Modeled online interactions and generated counterfactual trajectories via the simulator.
• Incorporated an adversarial network to mitigate bias and explore a wider range of treatment actions with scaled rewards.

Main Results:

• The proposed model demonstrated superior performance compared to existing methods.
• Evaluated on both simulated and real-world datasets, confirming its effectiveness.
• Successfully optimized treatment policies using offline EHR data.

Conclusions:

• The novel offline RL approach provides an effective solution for dynamic treatment regimes.
• This method reduces the need for expensive and time-consuming clinical interactions.
• Offers a new pathway for advancing precision medicine through data-driven treatment optimization.