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Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic

Nakeya Dewaswala1, David Chen2, Huzefa Bhopalwala1

  • 1Department of Cardiovascular Medicine, Mayo Clinic Rochester, Rochester, MN, USA.

BMC Medical Informatics and Decision Making
|October 18, 2022
PubMed

Insights

Natural language processing (NLP) accurately extracts hypertrophic cardiomyopathy (HCM) diagnoses from cardiac magnetic resonance (CMR) reports. This AI method significantly improves efficiency and accuracy over manual review for HCM patient data.

Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Natural Language Processing (NLP) for Clinical Data Extraction
  • Cardiovascular Imaging and Diagnostics

Background:

  • Cardiac magnetic resonance (CMR) imaging is crucial for diagnosing and assessing risk in hypertrophic cardiomyopathy (HCM) patients.
  • Manual review of CMR reports is inefficient, error-prone, and costly for large datasets.
  • Natural language processing (NLP) offers automated information extraction from clinical text in electronic health records (EHR).

Purpose of the Study:

  • To evaluate the accuracy of an NLP system in extracting hypertrophic cardiomyopathy (HCM) diagnoses from CMR reports.
  • To assess NLP's capability in extracting categorical and numerical data for HCM classification.

Main Methods:

  • Developed a two-tiered NLP system for information extraction from CMR reports.
  • The first tier focused on HCM diagnosis extraction; the second on classification concepts.
  • Randomly allocated 200 HCM patients (185 reports for training, 206 for testing) into training and testing sets.

Main Results:

  • NLP algorithms achieved very high performance compared to manual annotation.
  • The algorithm for HCM diagnosis extraction demonstrated an accuracy of 0.99.
  • High accuracies were reported for categorical concepts (e.g., HCM morphologic subtype 0.99) and numeric concepts (e.g., maximal LV wall thickness 0.96).

Conclusions:

  • NLP effectively identifies and classifies hypertrophic cardiomyopathy (HCM) from narrative CMR reports.
  • The developed NLP system shows very high performance, indicating its potential for clinical application.
Abstract

Keywords:
Cardiac magnetic resonance imagingHypertrophic cardiomyopathyNatural language processingRadiology reports

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