Automated abstraction of myocardial perfusion imaging reports using natural language processing

Chengyi Zheng, Benjamin C. Sun, Yi Lin Wu, Maros Ferencik, Ming Sum Lee, Rita F. Redberg, Aniket A. Kawatkar, Visanee V. Musigdilok, Adam L. Sharp

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Findings and interpretations of myocardial perfusion imaging (MPI) studies are documented in free-text MPI reports. MPI results are essential for research, but manual review is prohibitively time consuming. This study aimed to develop and validate an automated method to abstract MPI reports. Methods: We developed a natural language processing (NLP) algorithm to abstract MPI reports. Randomly selected reports were double-blindly reviewed by two cardiologists to validate the NLP algorithm. Secondary analyses were performed to describe patient outcomes based on abstracted-MPI results on 16,957 MPI tests from adult patients evaluated for suspected ACS. Results: The NLP algorithm achieved high sensitivity (96.7%) and specificity (98.9%) on the MPI categorical results and had a similar degree of agreement compared to the physician reviewers. Patients with abnormal MPI results had higher rates of 30-day acute myocardial infarction or death compared to patients with normal results. We identified issues related to the quality of the reports that not only affect communication with referring physicians but also challenges for automated abstraction. Conclusion: NLP is an accurate and efficient strategy to abstract results from the free-text MPI reports. Our findings will facilitate future research to understand the benefits of MPI studies but requires validation in other settings.

Original languageEnglish (US)
JournalJournal of Nuclear Cardiology
DOIs
StateAccepted/In press - 2020

Keywords

  • Myocardial perfusion imaging
  • data abstraction
  • ischemia
  • natural language processing
  • noninvasive stress test
  • nuclear cardiology

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine

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