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Related Concept Videos

Mesh Analysis01:20

Mesh Analysis

Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
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Classification of Leukocytes

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Classification of Bones01:18

Classification of Bones

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Long and Short Bones
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Classification of Connective Tissues01:30

Classification of Connective Tissues

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Updated: May 31, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Improving MeSH classification of biomedical articles using citation contexts.

Bader Aljaber1, David Martinez, Nicola Stokes

  • 1Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia.

Journal of Biomedical Informatics
|June 21, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for automatically assigning Medical Subject Headings (MeSH) to biomedical articles by analyzing citation references. This approach enhances information retrieval and classification accuracy.

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

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Last Updated: May 31, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

  • Biomedical Informatics
  • Information Retrieval
  • Natural Language Processing

Background:

  • Medical Subject Headings (MeSH) are crucial for indexing and retrieving biomedical literature in databases like PubMed.
  • Current methods for assigning MeSH terms may not fully leverage all available information for optimal accuracy.

Purpose of the Study:

  • To propose and evaluate a novel method for automating MeSH term assignment using citation references.
  • To enhance document feature representation for improved text mining and information retrieval applications.

Main Methods:

  • Developed a method to analyze citation references (citation contexts) to extract relevant terms not present in the original document.
  • Explored weighting schemes for citation terms based on their appearance section and distance to the citation marker.
  • Conducted intrinsic evaluations using the UMLS Metathesaurus and extrinsic evaluations via MeSH term classification experiments.

Main Results:

  • Citation contexts provide valuable terms related to the original document, improving feature representation.
  • The proposed method significantly outperforms two state-of-the-art MeSH classification systems (MeSHUP and MTI).
  • Weighting citation terms by section and distance leads to statistically significant improvements in feature quality.

Conclusions:

  • Analyzing citation contexts is an effective strategy for enhancing automated MeSH term assignment.
  • The novel approach offers a richer document feature representation, benefiting biomedical text processing and information retrieval.
  • This method holds promise for improving the discoverability and accessibility of biomedical information.