# Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks

arXiv: https://arxiv.org/abs/1904.11115 · HTML: https://mlsanity.com/paper/1904.11115/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2019.

## Built on by (most cited first, 8 of 8)

- [On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects](https://mlsanity.com/paper/2301.00512/) (2023)
- [The hidden risks of temporal resampling in clinical reinforcement learning](https://mlsanity.com/paper/2602.06603/) (2026)
- [A optimization framework for herbal prescription planning based on deep reinforcement learning](https://mlsanity.com/paper/2304.12828/) (2023)
- [Deep Attention Q-Network for Personalized Treatment Recommendation](https://mlsanity.com/paper/2307.01519/) (2023)
- [Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain](https://mlsanity.com/paper/2402.19226/) (2024)
- [An Improved Strategy for Blood Glucose Control Using Multi-Step Deep Reinforcement Learning](https://mlsanity.com/paper/2403.07566/) (2024)
- [Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning](https://mlsanity.com/paper/2505.21596/) (2025)
- [On Safer Reinforcement Learning for Sedation and Analgesia in Intensive Care](https://mlsanity.com/paper/2601.23154/) (2026)

## Abstract (arXiv)

Opioids are the preferred medications for the treatment of pain in the intensive care unit. While undertreatment leads to unrelieved pain and poor clinical outcomes, excessive use of opioids puts patients at risk of experiencing multiple adverse effects. In this work, we present a sequential decision making framework for opioid dosing based on deep reinforcement learning. It provides real-time clinically interpretable dosing recommendations, personalized according to each patient's evolving pain and physiological condition. We focus on morphine, one of the most commonly prescribed opioids. To train and evaluate the model, we used retrospective data from the publicly available MIMIC-3 database. Our results demonstrate that reinforcement learning may be used to aid decision making in the intensive care setting by providing personalized pain management interventions.