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

965
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...
965
Causality in Epidemiology01:21

Causality in Epidemiology

714
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
714
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.0K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
1.0K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

120
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
120
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

102
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
102
Vaccinations01:51

Vaccinations

45.2K
Overview
45.2K

You might also read

Related Articles

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

Sort by
Same author

Intracellular structural modifications of natural peptidoglycan fragments preceding NOD2 signaling in mammalian cells.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Serum Response Factor Regulates CCN1 to Exacerbate Acute Kidney Injury Through Facilitation of Ferroptosis-Related Injury.

Nephrology (Carlton, Vic.)·2026
Same author

The potential mechanisms of exercise-regulated mechanically sensitive ion channels in promoting spinal cord injury repair: a hypothesis-driven narrative review.

Reviews in the neurosciences·2026
Same author

Reduced Indocyanine Green Clearance Is Associated with Enteral Feeding Intolerance in Septic Patients Without Overt Liver Injury.

Journal of clinical medicine·2026
Same author

LabSage: Structural-Semantic Decoupling for Enhanced Retrieval-Augmented Generation in Clinical Laboratories.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

A multimodal generative model for structured and unstructured electronic health records.

npj health systems·2026

Related Experiment Video

Updated: Aug 30, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

Modeling COVID-19 Vaccine Adverse Effects with a Visualized Knowledge Graph Database.

Zhiyuan Liu1, Ximing Gao1, Chenyu Li2

  • 1Stanford Center for Professional Development, Stanford University, Stanford, CA 94305, USA.

Healthcare (Basel, Switzerland)
|August 26, 2022
PubMed
Summary
This summary is machine-generated.

This study used machine learning and ontology to analyze COVID-19 vaccine side effects from the Vaccine Adverse Event Reporting System (VAERS). The findings offer insights into potential adverse events and a visualized interface for healthcare professionals.

Keywords:
COVID-19 vaccineadverse effectsgraph embeddingsgraph representational learningknowledge graph database modeling

More Related Videos

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.2K
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

547

Related Experiment Videos

Last Updated: Aug 30, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.2K
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

547

Area of Science:

  • Pharmacovigilance
  • Medical Informatics
  • Computational Biology

Background:

  • The Vaccine Adverse Event Reporting System (VAERS) collects data on vaccine side effects.
  • Analyzing large datasets of vaccine adverse events is crucial for public health.
  • Understanding COVID-19 vaccine adverse events requires advanced analytical methods.

Purpose of the Study:

  • To analyze COVID-19 vaccine adverse events using ontology and machine learning.
  • To develop a visualized interface for accessing vaccine side effect information.
  • To predict key symptoms associated with hospitalization after vaccination.

Main Methods:

  • Utilized ontology and machine learning techniques.
  • Analyzed data from the Vaccine Adverse Event Reporting System (VAERS) up to March 2022.
  • Implemented a relational/graph database and developed an API for a visualized interface.
  • Employed a confusion matrix to evaluate prediction model performance.

Main Results:

  • Summarized side effects of COVID-19 vaccines.
  • Created a network of vaccine adverse effects.
  • Developed a user-friendly visualized interface for healthcare providers and patients.
  • Identified key symptoms predicting hospitalization and treatment post-vaccination.

Conclusions:

  • Ontology and machine learning provide valuable insights into vaccine adverse events.
  • The developed interface enhances accessibility to vaccine side effect information.
  • The methodology can be extended to all Food and Drug Administration (FDA)-approved vaccines.