Automated abstraction of myocardial perfusion imaging reports using natural language processing
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Research and Evaluation Department, Kaiser Permanente Southern California, Pasadena, CA Department of Emergency Medicine and Leonard Davis Institute, University of Pennsylvania, Philadelphia, PA Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR Division of Cardiology, Kaiser Permanente Southern California, Los Angeles Medical Center, Los Angeles, CA Division of Cardiology, University of California, San Francisco, San Francisco, CA
Received Jul 9, 2020; accepted Sep 29, 2020 doi:10.1007/s12350-020-02401-z
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. (J Nucl Cardiol 2020) Key Words: Myocardial perfusion imaging Æ data abstraction Æ ischemia Æ noninvasive stress test Æ natural language processing Æ nuclear cardiology Funding This work was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number R01HL134647. The authors have also provided an audio summary of the article, which is available to download as ESM, or to listen to via the JNC/ASNC Podcast. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12350-020-02401-z) contains supplementary material, which is available to authorized users. The authors of this article have provided a PowerPoint file, available for download at SpringerLink, which summarizes the contents of the
paper and is free for re-use at meetings and presentations. Search for the article DOI on SpringerLink.com. Reprint requests: Chengyi Zheng, PhD MS, Research and Evaluation Department, Kaiser Permanente Southern California, 100 S Los Robles Ave, 2nd Floor, Pasadena, CA 91101; [email protected] 1071-3581/$34.00 Copyr
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