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Annotating and detecting phenotypic information for chronic obstructive pulmonary disease.

Meizhi Ju1, Andrea D Short2, Paul Thompson1

  • 1National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, UK.

JAMIA Open
|January 28, 2020
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel annotated corpus and a neural network to identify detailed chronic obstructive pulmonary disease (COPD) phenotypes from text. This method efficiently extracts complex phenotypic information, aiding in understanding disease characteristics and treatment responses.

Keywords:
chronic obstructive pulmonary diseaseinformation extractionnatural language processingphenotypetext mining

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Area of Science:

  • Computational linguistics
  • Medical informatics
  • Pulmonology

Background:

  • Chronic obstructive pulmonary disease (COPD) encompasses diverse lung abnormalities.
  • Identifying specific COPD phenotypes from textual data is challenging due to complex information.
  • Existing text mining methods require enhancement for fine-grained phenotypic information extraction.

Purpose of the Study:

  • To develop a novel annotated corpus for chronic obstructive pulmonary disease (COPD) phenotypes.
  • To train a neural network-based named entity recognizer for detecting fine-grained COPD phenotypic information.
  • To enable text mining for complex COPD phenotype identification.

Main Methods:

  • Created an annotated corpus of 30 full papers with 27,030 phenotype-related concept mentions.
  • Annotated both outermost phenotype descriptions and nested concepts (proteins, treatments).
  • Developed a layered bidirectional long short-term memory conditional random field (BiLSTM-CRF) network to recognize nested and enclosing mentions.

Main Results:

  • The corpus is available online and linked to UMLS Metathesaurus concepts.
  • The trained BiLSTM-CRF network outperformed other popular approaches in recognizing detailed phenotypic information.
  • The method successfully extracts phenotypic information at various levels of granularity.

Conclusions:

  • The developed corpus and method facilitate efficient extraction of detailed COPD phenotype information.
  • Extracted information can aid in understanding treatment reactions and other specific phenotype details.
  • The approach requires minimal human intervention and can be adapted for other diseases.