Why the study?
Although NLP has been used retrospectively to identify patients with AAA, no studies have reported using NLP to identify AAA cases in near real-time from radiology reports.
Does a rule-based NLP algorithm accurately identify abdominal aortic aneurysm diagnoses from radiology reports in near real-time compared to manual physician review?
Does a rule-based NLP algorithm accurately identify abdominal aortic aneurysm diagnoses from radiology reports in near real-time compared to manual physician review?
A rule-based NLP algorithm can accurately identify abdominal aortic aneurysm cases from radiology reports in near real-time, supporting automated clinical decision support.
NLP for near real-time AAA detection from radiology reports is feasible in principle; leaves open clinical utility and prospective validation.
BACKGROUND Management of abdominal aortic aneurysms (AAAs) requires serial imaging surveillance to evaluate the aneurysm dimension. Natural language processing (NLP) has been previously developed to retrospectively identify patients with AAA from electronic health records (EHRs). However, there are no reported studies that use NLP to identify patients with AAA in near real-time from radiology reports. OBJECTIVE This study aims to develop and validate a rule-based NLP algorithm for near real-time automatic extraction of AAA diagnosis from radiology reports for case identification. METHODS The AAA-NLP algorithm was developed and deployed to an EHR big data infrastructure for near real-time processing of radiology reports from May 1, 2019, to September 2020. NLP extracted named entities for AAA case identification and classified subjects as cases and controls. The reference standard to assess algorithm performance was a manual review of processed radiology reports by trained physicians following standardized criteria. Reviewers were blinded to the diagnosis of each subject. The AAA-NLP algorithm was refined in 3 successive iterations. For each iteration, the AAA-NLP algorithm was modified based on performance compared to the reference standard. RESULTS A total of 360 reports were reviewed, of which 120 radiology reports were randomly selected for each iteration. At each iteration, the AAA-NLP algorithm performance improved. The algorithm identified AAA cases in near real-time with high positive predictive value (0.98), sensitivity (0.95), specificity (0.98), F1 score (0.97), and accuracy (0.97). CONCLUSIONS Implementation of NLP for accurate identification of AAA cases from radiology reports with high performance in near real time is feasible. This NLP technique will support automated input for patient care and clinical decision support tools for the management of patients with AAA.
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Gaviria-Valencia et al. (2022) studied this question.
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