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

Related Concept Videos

Reinforcement

Reinforcement

Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Reinforcement Schedules

Reinforcement Schedules

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,...

You might also read

Related Articles

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

Sort by

Same author

Beyond GPT-NER: ChatGPT as Ensemble Arbitrator for Discontinuous Named Entity Recognition in Health Corpora.

Studies in health technology and informatics·2025

Same author

Stacking Ensemble of Disproportionality Indicators for Adverse Vaccine Reactions Detection-An Empirical Study on Predicting Adverse Reactions of COVID-19 Vaccines.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023

Same author

Adjuvant probiotic Bifidobacterium animalis subsp. lactis CP-9 improve phototherapeutic treatment outcomes in neonatal jaundice among full-term newborns: A randomized double-blind clinical study.

Medicine·2022

Same author

Combinatorial Inactivation of Tumor Suppressors Efficiently Initiates Lung Adenocarcinoma with Therapeutic Vulnerabilities.

Cancer research·2022

Same author

Probiotic Strains Isolated from an Olympic Woman's Weightlifting Gold Medalist Increase Weight Loss and Exercise Performance in a Mouse Model.

Nutrients·2022

Same author

Adjuvant Probiotics of <i>Lactobacillus salivarius</i> subsp. <i>salicinius</i> AP-32, <i>L. johnsonii</i> MH-68, and <i>Bifidobacterium animalis</i> subsp. <i>lactis</i> CP-9 Attenuate Glycemic Levels and Inflammatory Cytokines in Patients With Type 1 Diabetes Mellitus.

Frontiers in endocrinology·2022

Search research articles

Related Experiment Video

Updated: Jun 8, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

ADR-DQPU: A Novel ADR Signal Detection Using Deep Reinforcement and Positive-Unlabeled Learning.

Chun-Kit Chung, Wen-Yang Lin

IEEE Journal of Biomedical and Health Informatics

|November 5, 2024

Summary

This summary is machine-generated.

Detecting adverse drug reactions (ADRs) is challenging due to data limitations. A new method, ADR-DQPU, integrates deep reinforcement Q-learning and positive-unlabeled learning to improve ADR signal detection from spontaneous reporting systems.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 8, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Pharmacovigilance
Machine Learning
Data Science

Background:

Spontaneous Reporting Systems (SRSs) like FAERS face challenges in analyzing and detecting severe adverse drug reactions (ADRs) due to unverified data and inherent uncertainties.
Limitations in SRS data hinder the development of robust machine learning models for ADR signal detection.
Authoritative knowledge bases (e.g., SIDER, BioSNAP) offer limited confirmed ADR relationships, resulting in small positive training sets against abundant unlabeled data.

Purpose of the Study:

To propose a novel method, ADR-DQPU, for improved ADR signal detection from SRS data.
To address the challenges of limited verified data and large unlabeled datasets in ADR detection.
To enhance the accuracy and efficiency of identifying potential adverse drug reactions.

Main Methods:

Integration of deep reinforcement Q-learning with positive-unlabeled learning techniques.
Development of the ADR-DQPU model specifically designed for ADR signal detection.
Validation of the proposed method using the FDA Adverse Event Reporting System (FAERS) dataset.

Main Results:

ADR-DQPU significantly outperformed six traditional methods in accuracy (26.45% overall improvement) and recall (18.57% improvement).
Compared to state-of-the-art machine learning methods, ADR-DQPU showed a 64.1% overall accuracy improvement and a 55.56% recall improvement.
The model achieved substantial improvements in F1 score (10.95% vs. traditional, 45.53% vs. state-of-the-art) and average accuracy.

Conclusions:

The ADR-DQPU method effectively enhances ADR signal detection from SRS data by leveraging advanced machine learning techniques.
The proposed approach offers a robust solution for identifying adverse drug reactions, overcoming limitations of traditional methods and existing datasets.
ADR-DQPU demonstrates significant potential for improving drug safety monitoring and pharmacovigilance through more accurate and efficient ADR detection.