Deep Reinforcement Learning for Safe Opioid Dosing in Critical Care

Overview

This project explored how deep reinforcement learning (RL) can be used to optimize opioid dosing in intensive care, addressing one of the most delicate trade-offs in modern medicine: relieving pain while minimizing the risk of opioid-related harm.

Using large-scale ICU data from the MIMIC-III database, we developed a sequential decision-making framework that learns personalized morphine dosing policies conditioned on each patient’s evolving pain scores and physiological state. The system produces clinically interpretable recommendations designed to reduce pain while maintaining heart rate and respiratory safety — a critical concern in opioid administration.

This work was among the first applications of deep reinforcement learning to automated pain management, and contributed to early thinking around how AI systems could support safer, data-driven medication decisions in high-stakes clinical environments.

Media coverage

• HealthExec: Opioid epidemic may have new nemesis in AI-based ‘deep reinforcement learning’
• Psychology Today: Using AI to Manage Opioid Use in Hospital ICUs
• Physician-Patient Alliance for Health & Safety: 3 Ideas to Improve Patient Care