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

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.6K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.6K
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

790
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
790
Documentation in Long-Term and Home Healthcare Setting01:29

Documentation in Long-Term and Home Healthcare Setting

868
Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
Long-Term Care Facilities
868
Ethical Standards I01:25

Ethical Standards I

769
The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
The Code of Ethics provisions outline the nurse's duty to the patient, the healthcare team, the profession, and society. The Code's fundamental principles include advocacy,...
769
Healthcare Agencies I01:18

Healthcare Agencies I

699
Healthcare agencies provide healthcare services to people. In the United States, voluntary agencies are often non-profit centers sponsored by donations, grants, or fundraisers. One such organization is Meals on Wheels, which provides meals to the elderly and homebound. The American Heart Association and the American Lung Association are other non-profit community organizations. Doctors and nurses are frequently active members of these organizations, which offer health checks and educational...
699
Integrated Healthcare System01:20

Integrated Healthcare System

1.5K
An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...
1.5K

You might also read

Related Articles

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

Sort by
Same author

CD44 expression associates with EBV LMP1, reduced CD8⁺ T cell infiltration, and lower PD-L1 combined positive score in nasopharyngeal carcinoma.

Infectious agents and cancer·2026
Same author

Deep Learning-Assisted Differentiation of Four Peripheral Neuropathies Using Corneal Confocal Microscopy.

Annals of clinical and translational neurology·2025
Same author

Exome sequencing reveals new insights into the germline landscape of inflammatory breast cancer among Tunisian patients.

Journal of translational medicine·2025
Same author

Correction: Blockchain-based zero trust networks with federated transfer learning for IoT security in industry 5.0.

PloS one·2025
Same author

Nasopharyngeal low-grade papillary Adenocarcinoma: A rare entity with diagnostic challenges.

Oral oncology·2025
Same author

Blockchain-based zero trust networks with federated transfer learning for IoT security in industry 5.0.

PloS one·2025

Related Experiment Video

Updated: May 25, 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

1.5K

Self-supervised learning for graph-structured data in healthcare applications: A comprehensive review.

Safa Ben Atitallah1, Chaima Ben Rabah2, Maha Driss1

  • 1Robotics and Internet of Things Laboratory, Prince Sultan University, Riyadh, 12435, Saudi Arabia; RIADI Laboratory, National School of Computer Science, University of Manouba, Manouba, 2010, Tunisia.

Computers in Biology and Medicine
|February 25, 2025
PubMed
Summary

This review explores self-supervised learning (SSL) for graph-structured healthcare data. SSL effectively leverages unlabeled data for improved disease prediction, medical imaging, and drug discovery.

Keywords:
Disease diagnosisDrug discoveryGraph representation learningHealthcare applicationsMedical imagingSelf-supervised learning

More Related Videos

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

8.2K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

Related Experiment Videos

Last Updated: May 25, 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

1.5K
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

8.2K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

Area of Science:

  • Graph-structured data analysis
  • Machine learning in healthcare
  • Biomedical informatics

Background:

  • Healthcare data is increasingly complex and interconnected.
  • Graph data structures excel at modeling these relationships.
  • Limited labeled data necessitates advanced learning techniques like self-supervised learning (SSL).

Purpose of the Study:

  • To provide a comprehensive review of SSL methods for graph-structured data in healthcare.
  • To explore challenges and opportunities in applying SSL to healthcare data.
  • To assess the effectiveness of SSL in various healthcare applications.

Main Methods:

  • Systematic review of existing literature on SSL for graph data in healthcare.
  • Analysis of SSL techniques applied to disease prediction, medical image analysis, and drug discovery.
  • Critical evaluation of SSL method performance, strengths, and limitations.

Main Results:

  • SSL offers a powerful paradigm for learning representations from unlabeled graph-structured healthcare data.
  • SSL techniques show promise across diverse healthcare applications, including prediction, diagnosis, and discovery.
  • The review identifies key challenges and future research directions for SSL in this domain.

Conclusions:

  • Self-supervised learning is a crucial tool for unlocking the potential of graph-structured healthcare data.
  • This paper serves as a foundational resource for researchers and practitioners in the field.
  • SSL application in healthcare graph data is poised to significantly enhance patient outcomes and accelerate medical advancements.