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Natural language processing of clinical notes for identification of critical limb ischemia

Naveed Afzal1, Vishnu Priya Mallipeddi2, Sunghwan Sohn1

  • 1Department of Health Sciences Research, Mayo Clinic and Mayo Foundation, Rochester, MN, United States.

International Journal of Medical Informatics
|February 10, 2018
PubMed

Insights

A new natural language processing (NLP) algorithm, CLI-NLP, accurately identifies critical limb ischemia (CLI) cases from electronic health records. This automated method shows superior performance compared to traditional billing codes for critical limb ischemia diagnosis.

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Cardiovascular Medicine

Background:

  • Critical limb ischemia (CLI) is an advanced stage of peripheral artery disease (PAD).
  • Accurate identification of CLI cases from electronic health records (EHRs) is difficult due to the lack of specific diagnostic codes.
  • Current diagnostic methods rely on clinical signs and symptoms, posing challenges for automated case finding.

Purpose of the Study:

  • To develop and validate a natural language processing (NLP) algorithm, termed CLI-NLP, for identifying CLI cases within clinical notes.
  • To compare the performance of the CLI-NLP algorithm against existing CLI-related billing codes (ICD-9).
  • To establish a reliable automated method for CLI case identification in EHRs.

Main Methods:

  • An existing NLP algorithm for PAD identification was extended to create the CLI-NLP algorithm.
  • The CLI-NLP algorithm was applied to clinical notes from EHRs to identify CLI cases.
  • Performance was evaluated against a gold standard of human abstraction and compared with ICD-9 billing codes, assessing metrics like positive predictive value (PPV), specificity, sensitivity, and F1-score.

Main Results:

  • The CLI-NLP algorithm demonstrated significantly higher PPV (96% vs. 67%) and specificity (98% vs. 74%) compared to ICD-9 billing codes.
  • The CLI-NLP algorithm achieved a higher F1-score (90% vs. 76%) than billing codes.
  • Sensitivity was comparable between the CLI-NLP algorithm (84%) and billing codes (88%).

Conclusions:

  • The CLI-NLP algorithm provides an accurate and efficient method for identifying critical limb ischemia from clinical notes in EHRs.
  • This NLP tool has excellent positive predictive value, suggesting strong reliability for automated case detection.
  • The CLI-NLP algorithm holds significant potential for improving patient care through automated case identification for quality initiatives, clinical decision support, and advancing a learning healthcare system.
Abstract

Keywords:
Critical limb ischemiaElectronic health recordsNatural language processingPeripheral artery diseaseSubphenotyping

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