Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.1K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.1K
Reinforcement Schedules01:24

Reinforcement Schedules

237
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
237
Operant Conditioning Intervention01:24

Operant Conditioning Intervention

110
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...
110
Chemotherapy-Induced Nausea and Vomiting: Dopamine Receptor Antagonists01:29

Chemotherapy-Induced Nausea and Vomiting: Dopamine Receptor Antagonists

436
Dopamine receptor antagonists, also known as antipsychotic agents, are critical in managing chemotherapy-induced vomiting. These antiemetic agents block dopamine receptors in the chemoreceptor trigger zone (CTZ), inhibiting signal transmission to the vomiting center. Antipsychotic agents encompass phenothiazines (PTZ), butyrophenones, benzamides, and thienobenzodiazepines (Zyprexa), which are utilized for their antiemetic and sedative properties.
Phenothiazines, such as prochlorperazine...
436
Drug Dosage Regimen: Overview01:15

Drug Dosage Regimen: Overview

3.8K
A drug dosage regimen describes the specific instructions and schedule for administering a drug to a patient. It considers factors such as drug dosage, frequency, route of administration, and duration of treatment. Designing an appropriate dosage regimen for a patient aims to achieve a target drug concentration at the site of action.
Typically, the starting dose and dosing interval are guided by the manufacturer's recommendations based on clinical trials conducted during and after drug...
3.8K

You might also read

Related Articles

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

Sort by
Same author

Impact of Procedural-Imaging Configurations on Radiation Dose During Endovascular Flow Diverter Treatment for Intracranial Aneurysms: A Comparison Between Hybrid Operating Room and Neuroangiography Suite.

Biomedicines·2026
Same author

Decisional processes balance sensitivity and stability in visual perception.

BMC biology·2026
Same author

Population Pharmacokinetics and Cerebrospinal Fluid Penetration of Intravenous Vancomycin in Intracranial Hemorrhage Patients with External Ventricular Drains: Implications for Dosing and Therapeutic Drug Monitoring.

Drug design, development and therapy·2026
Same author

Is repulsive serial bias in visual perception driven by adaptation mechanisms?

Journal of vision·2026
Same author

MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation.

Bioengineering (Basel, Switzerland)·2026
Same author

Renal function and post-thrombectomy outcomes and safety: nationwide registry study.

Journal of neurointerventional surgery·2025

Related Experiment Video

Updated: Sep 7, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Supervised Optimal Chemotherapy Regimen Based on Offline Reinforcement Learning.

Chamani Shiranthika, Kuo-Wei Chen, Chung-Yih Wang

    IEEE Journal of Biomedical and Health Informatics
    |June 17, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a Supervised Optimal Chemotherapy Regimen (SOCR) approach using offline reinforcement learning to determine optimal cancer treatment schedules. Oncologist supervision stabilizes regimens, creating a supportive tool for clinical decision-making.

    More Related Videos

    Modeling Chemotherapy Resistant Leukemia In Vitro
    08:41

    Modeling Chemotherapy Resistant Leukemia In Vitro

    Published on: February 9, 2016

    9.2K
    Pavlovian Conditioned Approach Training in Rats
    06:57

    Pavlovian Conditioned Approach Training in Rats

    Published on: February 4, 2016

    11.0K

    Related Experiment Videos

    Last Updated: Sep 7, 2025

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.3K
    Modeling Chemotherapy Resistant Leukemia In Vitro
    08:41

    Modeling Chemotherapy Resistant Leukemia In Vitro

    Published on: February 9, 2016

    9.2K
    Pavlovian Conditioned Approach Training in Rats
    06:57

    Pavlovian Conditioned Approach Training in Rats

    Published on: February 4, 2016

    11.0K

    Area of Science:

    • Oncology
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Reinforcement learning (RL) shows promise in modeling real-world scenarios beyond games.
    • Predicting optimal treatment regimens from clinical data is gaining traction, but RL outputs require expert oversight.
    • Current RL applications in medicine necessitate careful supervision by healthcare professionals.

    Purpose of the Study:

    • To develop a Supervised Optimal Chemotherapy Regimen (SOCR) approach for optimizing chemotherapy dosing schedules in cancer patients.
    • To integrate clinical expertise from oncologists into RL-generated treatment recommendations.
    • To enhance the reliability and clinical applicability of RL in personalized cancer therapy.

    Main Methods:

    • Utilized Offline Reinforcement Learning, specifically a model-based architecture employing the Conservative Q-Learning (CQL) algorithm.
    • Developed the SOCR approach to incorporate previous treatment decisions of oncologists, adding clinical knowledge to algorithmic outputs.
    • Tested the model on a dataset of forty Stage-IV colon cancer patients receiving first-line chemotherapy.

    Main Results:

    • The SOCR approach demonstrated that oncologists' supervision stabilizes chemotherapy regimens.
    • Experimental results indicated that the framework effectively integrates clinical expertise with algorithmic predictions.
    • The model proved successful in supporting oncologists' treatment decisions for colon cancer patients.

    Conclusions:

    • The proposed SOCR framework can serve as a valuable supportive tool for oncologists in making chemotherapy treatment decisions.
    • Integrating expert knowledge into RL algorithms enhances the stability and clinical relevance of treatment recommendations.
    • This approach offers a pathway for more effective and personalized cancer treatment planning.