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

## TL;DR

This paper introduces a deep reinforcement learning framework that personalizes morphine dosing in ICU pain management, aiming to optimize pain relief while minimizing adverse effects.

## Contribution

It presents a novel sequential decision-making model using dueling double-deep Q networks for personalized opioid dosing based on real-time patient data.

## Key findings

- Reinforcement learning can provide effective personalized pain management recommendations.
- The model was trained and validated using the MIMIC-3 database.
- Results suggest potential for improving clinical decision support in ICU settings.

## Abstract

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.