Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Apr 18, 2026

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

Published on: February 20, 2021

DDRL:Dyna-Based Discriminative Reinforcement Learning for Optimizing Sepsis Treatment Pathways in Offline

Dohyeun Kim, Hwin Dol Park, Jae-Hun Choi

IEEE Journal of Biomedical and Health Informatics

|April 16, 2026

Summary

This summary is machine-generated.

Related Concept Videos

Operant Conditioning Intervention

Operant Conditioning Intervention

Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Antisense oligonucleotides targeting Ninjurin1 ameliorate the pathology of lead- and cadmium-induced chronic obstructive pulmonary disease in mice.

Respiratory research·2026

Same author

Association between sleep duration and thirst in a nationally representative cross-sectional survey.

Scientific reports·2026

Same author

A data-analytics framework for exploring regression associations in multivariate categorical data of firefighters' PTSD.

Journal of applied statistics·2026

Same author

Recent Advances in Electrospun Nanofibers for Triboelectric Nanogenerators: Performance Enhancement Strategies and Emerging Applications.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Red/NIR-Emissive, Cadmium-Free Quantum Dots: Synthesis, Luminescence Mechanisms, and Applications.

Sensors (Basel, Switzerland)·2026

Same author

Risk of depressive symptom burden across central disorders of hypersomnolence: A nationwide multicenter study.

Journal of psychosomatic research·2026

Same journal

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026

Same journal

NeuroBooster: a domain-informed self-supervised learning paradigm tailored for brain MRI analysis.

IEEE journal of biomedical and health informatics·2026

Same journal

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

IEEE journal of biomedical and health informatics·2026

Same journal

Improving Multi-Sensor Non-Invasive Glucose Detection through AI: A Domain Generalization Approach.

IEEE journal of biomedical and health informatics·2026

Same journal

Unmixing the Neck: Accurate Jugular Venous Pulse Detection From Wearable PPG.

IEEE journal of biomedical and health informatics·2026

Same journal

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

IEEE journal of biomedical and health informatics·2026

See all related articles

This study introduces Dyna-Based Discriminative Reinforcement Learning (DDRL) to optimize sepsis treatment policies. DDRL aligns AI decisions with physician practices, improving patient care and addressing physician shortages.

Area of Science:

Artificial Intelligence in Medicine
Clinical Decision Support Systems
Reinforcement Learning for Healthcare

Background:

Automated sepsis treatment policies using Reinforcement Learning (RL) aim to enhance care quality and mitigate physician shortages.
Offline RL faces challenges with limited exploration, leading to Q-value overestimation and suboptimal policies deviating from physician practices.

Purpose of the Study:

To develop a Dyna-Based Discriminative Reinforcement Learning (DDRL) method for optimal sepsis treatment policy learning.
To ensure the learned policy aligns with established physician treatment strategies.
To overcome limitations of offline RL in healthcare settings.

Main Methods:

DDRL integrates Electronic Medical Record (EMR) data with simulated treatment episodes.

Related Experiment Videos

Last Updated: Apr 18, 2026

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

Published on: February 20, 2021

A Discriminator component is employed to suppress Q-values for out-of-distribution treatments.

This approach mitigates Q-value overestimation and reduces policy deviation from physician behavior.

Main Results:

DDRL demonstrated superior performance compared to Conservative Q-Learning (CQL) and physician policies.
Expected returns for DDRL were 7.29 (Asan Medical Center) and 4.55 (Ajou University Hospital).
Cosine similarity between DDRL and physician policies reached 81.68% and 90.90%, significantly outperforming CQL.

Conclusions:

DDRL effectively learns optimal sepsis treatment policies that align with physician expertise.
The method successfully addresses offline RL limitations by incorporating EMR data and simulation.
DDRL shows promise for improving automated clinical decision-making in sepsis management.