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

Pharmacovigilance01:19

Pharmacovigilance

2.0K
Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
2.0K
Drug Toxicity: Risk factors01:24

Drug Toxicity: Risk factors

226
Adverse Drug Reactions (ADRs) are potential complications that arise during pharmacotherapy, influenced by multiple risk factors. Age plays a significant role; both neonates and the elderly are at heightened risk due to their respective immature and diminished metabolic and elimination processes. Gender also impacts ADRs, with females experiencing a 1.5 to 1.7-fold greater risk than males, which may be linked to pharmacokinetic, pharmacodynamic, and hormonal differences. Notably, neonates, the...
226
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.8K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
1.8K
Factors Affecting Drug Response: Overview01:21

Factors Affecting Drug Response: Overview

3.1K
When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
3.1K
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

8.2K
Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....
8.2K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

2.1K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
2.1K

You might also read

Related Articles

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

Sort by
Same author

Development and evaluation of a health-based heat wave categorization system: a case study of Seville, Spain.

International journal of biometeorology·2026
Same author

Rocuronium Dose and First-Attempt Intubation Success in the Critically Ill: Secondary Analysis of Two Multicenter Trials.

American journal of respiratory and critical care medicine·2026
Same author

Reducing ED Boarding Time as the Key to Minimizing Admission Delays: A Quality Improvement Initiative.

American journal of medical quality : the official journal of the American College of Medical Quality·2026
Same author

Towards a more reliable assessment of aortic diameters using a Bayesian Z-score.

Scientific reports·2026
Same author

United Global Advocacy Drives Updates to World Health Organization Essential Medicines List.

Haemophilia : the official journal of the World Federation of Hemophilia·2026
Same author

Impact of measurable residual disease on outcomes using a modified DFCI protocol for adults with BCR-ABL negative acute lymphoblastic leukemia.

Leukemia research·2026

Related Experiment Video

Updated: Apr 23, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

9.6K

Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events.

Jesse Davis1, Vítor Santos Costa2, Peggy Peissig3

  • 1KU Leuven, Celestijnenlaan 200a, Heverlee 3001, Belgium.

Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning
|October 7, 2014
PubMed
Summary

This study introduces a novel demand-driven clustering method for analyzing electronic medical records (EMR) to predict adverse drug reactions, outperforming existing approaches.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.8K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

3.4K

Related Experiment Videos

Last Updated: Apr 23, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

9.6K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.8K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

3.4K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Biomedical Informatics

Background:

  • Electronic medical records (EMR) present unique challenges for machine learning due to their complex relational structure and uncertain temporal dependencies.
  • Statistical relational learning methods are suitable for EMR analysis but struggle with inherent latent structures, like relationships between diseases or medications.
  • Existing approaches often rely on pre-defined clustering or expert knowledge, which may not fully capture the nuanced relationships within EMR data.

Purpose of the Study:

  • To develop and evaluate a novel machine learning approach for analyzing electronic medical records (EMR).
  • To improve the prediction of adverse drug reactions by effectively handling the latent structure within EMR data.
  • To introduce a demand-driven clustering technique that integrates object clustering directly into the learning process.

Main Methods:

  • Proposed a novel algorithm that performs demand-driven clustering during the learning phase, rather than relying on pre-clustering.
  • Applied statistical relational learning principles to model the complex relationships within EMR data.
  • Evaluated the algorithm on three real-world datasets focused on predicting adverse medication reactions.

Main Results:

  • The proposed demand-driven clustering approach demonstrated superior accuracy in predicting adverse drug reactions compared to methods without clustering.
  • The algorithm outperformed traditional pre-clustering techniques and the use of expert-constructed medical heterarchies.
  • The findings highlight the effectiveness of integrating clustering dynamically within the learning process for EMR analysis.

Conclusions:

  • Demand-driven clustering is a more effective strategy for uncovering latent structures in EMR data than pre-clustering or no clustering.
  • This novel approach significantly enhances the accuracy of predicting adverse drug reactions from electronic medical records.
  • The method offers a promising advancement for leveraging complex EMR data in clinical decision support and pharmacovigilance.