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

Time Course of Drug Effect01:14

Time Course of Drug Effect

2.1K
The progression of a drug's impact can be analyzed by examining both the concentration-time course and the effect-time course. The concentration-time course is determined by the drug's half-life and is influenced by factors such as its pharmacokinetics, including absorption, distribution, metabolism, and elimination. The effect of the drug is often related to its concentration in the plasma and is calculated using the maximum drug effect and the plasma concentration that generates 50...
2.1K
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

796
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...
796
Drug Dosage Regimen: Overview01:15

Drug Dosage Regimen: Overview

3.6K
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...
3.6K
Drug Administration and Therapy Phases: Overview01:26

Drug Administration and Therapy Phases: Overview

476
Drugs, the chemical agents used in diagnosing, treating, or preventing diseases, undergo a four-phase process of development: pharmaceutic, pharmacokinetics, pharmacodynamics, and therapeutic.
The pharmaceutical phase focuses on leveraging the physicochemical properties of the drug to design and manufacture an effective product. Variants include orally administered tablets or capsules, topical creams or ointments, and parenteral-delivery solutions or emulsions.
The pharmacokinetic phase...
476
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

729
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...
729
Combined Effects of Drugs: Antagonism01:30

Combined Effects of Drugs: Antagonism

8.5K
The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
The most common type is receptor antagonism, where one drug acts as an antagonist to block the effects of another drug by...
8.5K

You might also read

Related Articles

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

Sort by
Same author

Chemical Proteomics Reveals a Novel Long-Acting Maleimide Algaecide Targeting Glyceraldehyde-3-Phosphate Dehydrogenase for Cyanobacterial Bloom Control.

Journal of agricultural and food chemistry·2026
Same author

Yap mediates hippo signaling to balance proliferation and differentiation in the developing glandular stomach epithelium.

Cell reports·2026
Same author

DeepDOX1: A Dual-Drive Framework Integrating Deep Learning and First-Principles Quantum Chemistry for Drug-Protein Affinity Prediction.

JACS Au·2026
Same author

Stiffness-Activated Stellate Cells Drive Pancreatic Cancer Liver Colonization via GMFG-TNS4 Signaling.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Accuracy of machine learning in predicting recurrence risk of lumbar disc herniation after percutaneous endoscopic lumbar discectomy: a systematic review and meta-analysis.

Journal of orthopaedic surgery and research·2026
Same author

HiRMD: A System for Mortality Prediction via LLM-Based High-Risk Information Extraction and Diagnosis.

IEEE transactions on bio-medical engineering·2026

Related Experiment Video

Updated: Jul 10, 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

232

A-GSTCN: An Augmented Graph Structural-Temporal Convolution Network for Medication Recommendation Based on Electronic

Weiqi Yue1, Maiqiu Wang2, Lei Zhang1,2

  • 1School of Electronic and Information Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Bioengineering (Basel, Switzerland)
|November 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an augmented graph structural-temporal convolutional network (A-GSTCN) for improved medication recommendation from electronic health records (EHRs). The novel A-GSTCN model enhances accuracy by considering both structural and temporal data, outperforming existing methods.

Keywords:
dilated convolutionelectronic health recordsgraph structural-temporal convolutional networkmedication recommendation

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

594

Related Experiment Videos

Last Updated: Jul 10, 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

232
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

594

Area of Science:

  • Biomedical informatics
  • Artificial intelligence in healthcare
  • Machine learning for clinical decision support

Background:

  • Electronic health records (EHRs) are crucial for personalized medicine but pose challenges due to complex structural and temporal data.
  • Existing medication recommendation systems often fail to capture the intricate relationships between medical events or adequately utilize historical patient data.
  • This limitation leads to suboptimal prescription recommendations and reduced clinical decision-making quality.

Purpose of the Study:

  • To develop an advanced model for medication recommendation that effectively addresses the limitations of current approaches in handling EHR data.
  • To improve the accuracy and reliability of medication recommendations by integrating structural and temporal information from patient histories.
  • To enhance the learning of historical EHR data for more precise patient care.

Main Methods:

  • An augmented graph attention network was employed to model the structural correlations among diverse medical events within EHRs.
  • A dilated convolution with residual connections was utilized to enhance temporal prediction capabilities and reduce model complexity.
  • A cache memory module was integrated to improve the model's ability to learn from extensive historical EHR data.

Main Results:

  • The proposed augmented graph structural-temporal convolutional network (A-GSTCN) demonstrated superior performance compared to baseline methods.
  • Efficiency was validated using Jaccard index, F1 score, and PRAUC metrics, confirming the model's effectiveness.
  • The A-GSTCN model achieved a significant reduction in training parameters, reducing them by an order of magnitude.

Conclusions:

  • The A-GSTCN model offers a significant advancement in medication recommendation systems by effectively leveraging the structural and temporal dynamics of EHRs.
  • This approach provides a more accurate and computationally efficient solution for clinical decision support.
  • The findings suggest a promising direction for developing intelligent healthcare systems that can provide better patient care through improved medication suggestions.