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Identification and validation of respiratory virus immunization using natural language processing.

Kevin A Wilson1, John J Riddles1, Andrew C Hill2

  • 1Westat, Bethesda, MD, United States.

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Summary
This summary is machine-generated.

This study developed a natural language processing (NLP) algorithm to identify immunizations in electronic health records (EHRs). The NLP tool shows high accuracy for COVID-19 and influenza vaccines, enhancing EHR data.

Keywords:
COVID-19electronic health recordsimmunization verificationinfluenzanatural language processing (NLP)respiratory syncytial virus (RSV)rule-based methodsvaccine verification

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Public Health

Background:

  • Electronic health records (EHRs) often contain valuable immunization data within unstructured text notes.
  • Structured EHR data may not always capture the full scope of immunization administration.

Purpose of the Study:

  • To develop and evaluate a rule-based natural language processing (NLP) algorithm for identifying COVID-19, influenza, and RSV immunizations within EHR narratives.
  • To assess the performance of the NLP algorithm against manual review and structured EHR data.

Main Methods:

  • A multi-stage, rule-based NLP algorithm was developed using general and specific keywords, considering grammatical dependencies.
  • The algorithm incorporated rules for negation and immunization recommendations.
  • Performance was evaluated using precision and recall metrics on a sample of 20,000 patients, with manual review of 400 notes.

Main Results:

  • Manual review showed high precision and recall for COVID-19 (71%, 97%) and influenza (91%, 92%) immunizations.
  • RSV immunization identification achieved 57% precision and 96% recall during manual review.
  • Comparison with structured data yielded lower recall (9-12%) but acceptable precision (72-78%) for all vaccine types.

Conclusions:

  • The NLP algorithm effectively identifies immunizations mentioned in EHR text, particularly for COVID-19 and influenza.
  • Low recall against structured data indicates limitations in capturing all immunizations solely from text.
  • The algorithm offers specialized utility for augmenting immunization records and serves as a framework for future EHR research.