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Experiments in concept modeling for radiographic image reports

D S Bell1, E Pattison-Gordon, R A Greenes

  • 1Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.

Journal of the American Medical Informatics Association : JAMIA
|May 1, 1994
PubMed
Summary
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Researchers developed a concept model for chest radiography reporting to improve data entry and image retrieval. Manual modeling, guided by specific criteria, yielded a consistent structure, though automated methods were not found.

Area of Science:

  • Medical Informatics
  • Radiology
  • Natural Language Processing

Background:

  • Structured data entry and image retrieval in chest radiography are crucial for clinical workflows.
  • Current methods for chest radiographic reporting lack standardization, hindering data analysis and retrieval.

Purpose of the Study:

  • To develop methods for building concept models to support structured data entry and image retrieval in chest radiography.
  • To create an organizing model for chest radiographic reporting.

Main Methods:

  • Manual analysis of natural-language chest radiograph reports by clinician-informaticians.
  • Evaluation of conceptual structures based on clinical relevance, semantic constraints, canonical forms, and simplicity.
  • Application of the organizing model to represent sample reports and comparison with natural language processing outputs.

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Main Results:

  • An organizing model for chest radiography reporting was established, comprising 62 concept types and 17 relations in an inheritance network.
  • The model includes broad types such as finding, anatomic locus, procedure, attribute, and status, with diagnoses as a subtype of finding.
  • Representation of sample reports introduced 79 narrower concept types, and natural language processing outputs suggested potential associations.

Conclusions:

  • A manual modeling process using explicit criteria resulted in a consistent organizing model for chest radiography reporting.
  • Both top-down and bottom-up modeling approaches were necessary for model development.
  • While natural language processing can inform model building, fully automated algorithms for manual modeling were not identified; further research requires objective evaluation methods and formalization tools.