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

Opioids are the preferred medications for the treatment of pain in the intensive care unit. While under-treatment 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 decisi...

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Bibliographic Details
Main Authors: Lopez-Martinez, Daniel, Eschenfeldt, Patrick, Ostvar, Sassan, Ingram, Myles, Hur, Chin, Picard, Rosalind
Format: Conference Proceeding
Language:English
Subjects:
Heart rate
MIMICs
Pain
Physiology
Reinforcement learning
Training
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Summary:Opioids are the preferred medications for the treatment of pain in the intensive care unit. While under-treatment 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.
ISSN:1558-4615
2694-0604
DOI:10.1109/EMBC.2019.8857295