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Anatomical entity recognition with a hierarchical framework augmented by external resources.

Yan Xu1, Ji Hua2, Zhaoheng Ni2

  • 1State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China; Microsoft Research Asia, Beijing, China.

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|October 25, 2014
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Summary

This study introduces a hierarchical framework using named entity recognizers (NERs) to identify implicit anatomical entities in medical records. The approach improves the recognition of anatomical locations from diverse expressions like diseases and treatments.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Medical records contain explicit anatomical references and implicit mentions through diseases, tests, and treatments.
  • Accurate identification of all anatomical entities is crucial for medical data analysis and knowledge extraction.

Purpose of the Study:

  • To develop and evaluate a hierarchical framework for recognizing both explicit and implicit anatomical entities in medical records.
  • To enhance the extraction of anatomical information by leveraging diverse data sources and knowledge bases.

Main Methods:

  • A hierarchical framework with two layers of named entity recognizers (NERs) using Conditional Random Fields (CRF) was implemented.
  • A comprehensive dictionary of anatomical entity expressions was built using UMLS, MeSH, RadLex, and BodyPart3D.
  • External knowledge bases, Wikipedia and WordNet, were utilized to improve inference of implicit anatomical entities.

Main Results:

  • The system achieved 0.8137 F1 for explicit anatomical entity recognition and 0.7690 F1 for implicit recognition on 300 discharge summaries.
  • The hierarchical framework demonstrated a 5.08% increment in F1 by integrating diverse entity types and external knowledge.
  • The developed resources will be publicly available to the research community.

Conclusions:

  • The proposed hierarchical framework effectively identifies both explicit and implicit anatomical entities in clinical text.
  • Integrating multiple NER layers and external knowledge bases significantly enhances the accuracy of anatomical entity recognition.
  • This work contributes valuable, publicly available resources for advancing medical informatics and NLP research.