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

Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

You might also read

Related Articles

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

Sort by
Same author

Automatic classification of circulating blood cell clusters based on multi-channel flow cytometry imaging.

Engineering applications of artificial intelligence·2026
Same author

Evaluating RAG and Non-RAG Pipelines for Concept Discovery in Environmental Health Ontologies.

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

Single nuclei RNA-sequencing reveals transcriptional heterogeneity in the blastema of favorable histology Wilms tumor.

JCI insight·2026
Same author

Convergence to Steady State in LLM-Generated Ontological Concepts.

Studies in health technology and informatics·2026
Same author

An AI-enabled tool for quantifying overlapping red blood cell sickling dynamics in microfluidic assays.

Lab on a chip·2026
Same author

Decoupling Detection and Classification to Improve Morphological Phenotype Analysis of Sickle Red Blood Cells in Full-Scope Microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

The Essential Components and Critical Conditions for Success in a Learning Health System in Oncology.

Studies in health technology and informatics·2026
Same journal

Use of Artificial Intelligence in Screening for Adolescent Idiopathic Scoliosis: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Movement Related Biomechanics in Adolescent Idiopathic Scoliosis: A Review of Reviews.

Studies in health technology and informatics·2026
Same journal

The Impact of Surgical Correction of Adolescent Idiopathic Scoliosis Using Posterior Spinal Fusion on Selected Radiological Parameters and Respiratory Function.

Studies in health technology and informatics·2026
Same journal

Acute Effect of Physio-logic® Exercises on Muscle Tone and Stiffness in Adolescent Idiopathic Scoliosis Patients: A Preliminary Study.

Studies in health technology and informatics·2026
Same journal

Effects of Integrated Music and Occupational Therapy on Motor and Autonomic Function in Children with Neurogenic Scoliosis.

Studies in health technology and informatics·2026
See all related articles

Related Experiment Videos

Relational Graph Convolutional Network with BERT Embeddings for Ontology Relationship Classification.

T M Rubaith Bashar1, James Geller1, Mengjia Xu1

  • 1Department of Data Science, New Jersey Institute of Technology, USA.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method combining domain-specific BERT models with Relational Graph Convolutional Networks (RGCN) to classify relationships in SNOMED CT. The SapBERT-RGCN model achieved superior accuracy in this crucial task for medical ontology curation.

Keywords:
Domain-Specific BERTsRelational Graph Neural NetworkSNOMED CT

Related Experiment Videos

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • SNOMED CT is a comprehensive medical ontology vital for healthcare data.
  • Manually curating SNOMED CT relationships is labor-intensive and complex.
  • Accurate hierarchical placement and relationship classification are essential for ontology quality.

Purpose of the Study:

  • To develop an automated method for classifying relationships between SNOMED CT concept pairs.
  • To evaluate the effectiveness of integrating domain-specific BERT models with Relational Graph Convolutional Networks (RGCN).
  • To improve the efficiency and accuracy of SNOMED CT curation.

Main Methods:

  • Utilized five domain-specific BERT models (BioBERT, ClinicalBERT, SapBERT, SciBERT, BioMedBERT) for generating node embeddings.
  • Integrated BERT embeddings with a three-layer RGCN model for relationship classification.
  • Compared the performance of the proposed RGCN models against Message-Passing Neural Network (MPNN) and Neural Network (NN) baselines.

Main Results:

  • All BERT-RGCN models outperformed the MPNN and NN baselines.
  • The SapBERT-RGCN model demonstrated the highest performance, achieving 0.9394 accuracy and 0.9412 F1-weighted score.
  • SapBERT-RGCN showed a 4% accuracy gain and significant F1-macro improvement over the best BERT-NN baseline.

Conclusions:

  • The integration of domain-specific BERT embeddings with RGCN is an effective approach for SNOMED CT relationship classification.
  • The SapBERT-RGCN model offers a promising solution for automating and enhancing the accuracy of medical ontology curation.
  • This method can significantly reduce the manual effort required for maintaining large-scale medical terminologies.