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

Drug Dosage Regimen: Overview01:15

Drug Dosage Regimen: Overview

3.7K
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...
3.7K
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

974
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
974
Factors Affecting Drug Response: Overview01:21

Factors Affecting Drug Response: Overview

2.1K
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...
2.1K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

120
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
120
Dosage Regimen: Fixed Dose01:01

Dosage Regimen: Fixed Dose

1.9K
Fixed-dose regimens are a common approach to administer drugs to achieve and maintain desired levels of the drug in the body. In this dosing strategy, a specific amount of medication is given at regular intervals, often multiple times a day, to ensure a consistent drug concentration in the bloodstream.
Fixed-dose regimens can be used for various routes of administration, including intravenous (IV) injections and oral medications. For IV administration, a predetermined amount of the drug is...
1.9K
Prescription, Nonprescription and Orphan Drugs01:02

Prescription, Nonprescription and Orphan Drugs

813
Prescription drugs require a prescription from a medical practitioner and can only be obtained from a pharmacy. They have many applications, including treating pain, anxiety, and hypertension.
The misuse and addiction to prescription drugs is a growing problem that can affect people of all age groups, specifically teenagers. This can happen when prescription medications are used in ways not intended by the prescriber, such as taking someone else's prescription or using medication for...
813

You might also read

Related Articles

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

Sort by
Same author

CIMatcher: Cross-scale interaction matcher for accurate local feature matching.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Fiber-optic triggering of a two-stage high-current linear transformer driver with laser energy below 100 μJ.

The Review of scientific instruments·2026
Same author

Suppressing Interfacial-Accelerated Degradation in Perovskite Solar Cells via Supramolecular Co-Assembly.

Angewandte Chemie (International ed. in English)·2026
Same author

Retraction notice to "Study on the effect of SDBS and SDS on deep coal seam water injection" [Sci. Total Environ. 856 (2023) 158930].

The Science of the total environment·2026
Same author

Arginine-substituted Mastoparan-C derivatives combat dual bacterial pathogens: <i>in vitro</i> mechanistic insights and <i>in vivo</i> efficacy in polymicrobial wounds.

Microbiology spectrum·2026
Same author

Safety and efficacy of the MNK/VEGFR inhibitor JDB153 combined with the anti-PD-1 antibody serplulimab in patients with advanced pancreatic cancer refractory to standard treatment: a single-arm, phase Ib/II study protocol.

Frontiers in pharmacology·2026

Related Experiment Video

Updated: Aug 11, 2025

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

431

DGCL: Distance-wise and Graph Contrastive Learning for medication recommendation.

Xingwang Li1, Yijia Zhang1, Xiaobo Li1

  • 1School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China.

Journal of Biomedical Informatics
|February 6, 2023
PubMed
Summary

This study introduces a new framework for personalized medicine recommendations, improving accuracy and safety by considering both patient history and drug-drug interactions (DDIs). The DGCL model effectively reduces harmful DDIs while enhancing clinical decision-making.

Keywords:
Contrastive learningDrug drug interactionsGraph neural networkMedication recommendation

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
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

Related Experiment Videos

Last Updated: Aug 11, 2025

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

431
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
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

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Pharmacology

Background:

  • Personalized medicine recommendations are crucial for effective healthcare.
  • Current methods often fail to balance patient history analysis with drug-drug interaction (DDI) avoidance.
  • Existing approaches either neglect DDIs or lack patient data integration, leading to suboptimal accuracy and safety.

Purpose of the Study:

  • To develop a novel framework for personalized medication recommendations that integrates patient electronic health records (EHRs) with drug-drug interaction (DDI) data.
  • To enhance the accuracy and safety of medication recommendations by considering both historical patient data and potential drug interactions.
  • To address the limitations of existing methods in balancing patient-specific information with DDI management.

Main Methods:

  • Proposed the Distance-wise and Graph Contrastive Learning (DGCL) framework.
  • Developed a two-stage neural network for clinical record learning, incorporating a distance detection loss.
  • Implemented a graph contrastive learning method to jointly train DDI and electronic health record graphs for DDI control.

Main Results:

  • The DGCL framework demonstrated superior performance compared to baseline models on the MIMIC-III dataset.
  • Achieved improved efficacy in medication recommendations by effectively modeling patient historical data.
  • Significantly enhanced safety by controlling and reducing undesirable drug-drug interactions.

Conclusions:

  • The DGCL framework offers a robust solution for personalized medication recommendations, balancing patient history and DDI considerations.
  • This approach improves both the effectiveness and safety of clinical decision-making in medication prescription.
  • DGCL represents a significant advancement in leveraging AI for safer and more accurate healthcare.