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Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports

Yiqing Zhao1,2, Nooshin J Fesharaki1, Hongfang Liu2

  • 1Department of Health Informatics and Administration, Center for Biomedical Data and Language Processing, University of Wisconsin-Milwaukee, 2025 E Newport Ave, NWQ-B Room 6469, Milwaukee, WI, 53211, USA.

BMC Medical Informatics and Decision Making
|July 8, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data-driven method for creating knowledge models from medical imaging reports, improving information retrieval and knowledge discovery. The developed model achieved high accuracy in representing medical report content and relationships.

Keywords:
Big data analysisInformation extractionKnowledge modelingMedical imagingNatural language processingSemantic networkSublanguage analysisText mining

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Knowledge Representation

Background:

  • Knowledge models are crucial for information retrieval, knowledge base development, and decision support.
  • Existing machine learning methods for knowledge base construction often lack precision in entity and relationship extraction.

Purpose of the Study:

  • To describe a data-driven sublanguage pattern mining method for creating knowledge models.
  • To combine Natural Language Processing (NLP) and semantic network analysis for model generation.

Main Methods:

  • Utilized data from Radiopaedia.org, an open-source imaging case repository.
  • Extracted entities and relationships using Stanford part-of-speech parser and a "Subject:Relationship:Object" schema.
  • Tagged noun phrases with Unified Medical Language System (UMLS) semantic types.

Main Results:

  • Constructed a semantic type network from 23,410 medical image reports, identifying 135 UMLS semantic types.
  • Developed a knowledge model with 14 semantic categories, covering 98% of the evaluation corpus content and revealing 97% of relationships.
  • Achieved 87% precision, 79% recall, and 82% F-score in machine annotation.

Conclusions:

  • The developed pipeline successfully generated a comprehensive, content-based knowledge model.
  • The knowledge model effectively represents context from diverse sources within the medical imaging domain.