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An Information Retrieval Approach to ICD-10 Classification.

Hee Park1, José Castaño1, Pilar Ávila1

  • 1Departamento de Informática en Salud, Hospital Italiano de Buenos Aires, Argentina.

Studies in Health Technology and Informatics
|August 24, 2019
PubMed
Summary

This study presents an automatic method to map free-text disease descriptions to International Classification of Diseases 10th revision (ICD-10) codes. The approach utilizes search engine similarity metrics on indexed terminology data for accurate code retrieval.

Keywords:
Clinical CodingInternational Classification of DiseasesNatural Language Processing

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Area of Science:

  • Medical Informatics
  • Health Information Management

Background:

  • Accurate disease classification is crucial for healthcare.
  • Manual mapping of clinical notes to ICD-10 codes is time-consuming.
  • Automating this process can improve efficiency and data quality.

Purpose of the Study:

  • To develop and evaluate an automatic information retrieval approach.
  • To map free-text disease descriptions to ICD-10 codes.
  • To enhance the efficiency of clinical coding.

Main Methods:

  • Utilized Hospital Italiano de Buenos Aires (HIBA) terminology data.
  • Mapped HIBA data to ICD-10 codes for indexing.
  • Employed search engine similarity metrics for code retrieval.

Main Results:

  • Demonstrated an effective automatic information retrieval approach.
  • Successfully mapped free-text disease descriptions to ICD-10 codes.
  • Showcased the utility of search engine similarity metrics in this context.

Conclusions:

  • The proposed automatic method offers a viable solution for ICD-10 code mapping.
  • This approach can streamline clinical documentation and data analysis.
  • Further research can refine the accuracy and scope of the automated system.