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Related Concept Videos

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Knowledge of anatomy is essential to understand human biology and medicine. Anatomists and health care professionals use standard terminology to describe the human body with more precision and no ambiguity. Anatomical terms have mostly Greek and Latin-derived roots. Because these languages are rarely used in conversation, the meaning of words remains the same. Each term is made up of a root in between the prefixes and suffixes. The root of a term often refers to an organ, tissue, or condition,...
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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Quality documentation and reporting share essential characteristics that ensure they are practical and valuable resources for those who use them. These characteristics are:
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Terminology Coverage from Semantic Annotated Health Documents.

Marie Ndangang1, Julien Grosjean1, Romain Lelong1

  • 1Department of Biomedical Informatics, Rouen University Hospital, Normandy, France.

Studies in Health Technology and Informatics
|October 12, 2018
PubMed
Summary
This summary is machine-generated.

Semantic annotation of unstructured health documents identified millions of concepts. General terminologies like SNOMED CT, NCIt, and MeSH were most effective for concept identification in clinical data.

Keywords:
Big data analyticsSemantic Data WarehouseSemantic annotatorTerminology server

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Area of Science:

  • Medical Informatics
  • Clinical Data Analysis
  • Natural Language Processing in Healthcare

Background:

  • Unstructured health documents, such as discharge summaries, are crucial information sources.
  • Effective utilization of this data is essential for healthcare.
  • Challenges exist in extracting meaningful information from free-text clinical notes.

Purpose of the Study:

  • To evaluate the effectiveness of semantic annotation for concept identification in unstructured health documents.
  • To determine which terminologies yield the most annotations.
  • To analyze the relationship between terminology scope and concept identification rates.

Main Methods:

  • A semantic annotator was employed to identify clinical concepts.
  • The annotation process utilized a clinical data warehouse from Rouen University Hospital.
  • Concepts were extracted from approximately 11.9 million health documents.

Main Results:

  • Over 2 billion annotations were generated across the document corpus.
  • An average of 175 annotations were identified per document.
  • SNOMED CT, NCIt, and MeSH were the top three terminologies for concept annotation.

Conclusions:

  • General terminologies with a high number of translated concepts led to the most identified concepts.
  • The findings highlight the importance of terminology breadth in clinical data analysis.
  • Semantic annotation effectively extracts valuable concepts from unstructured clinical text.