Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study
BackgroundManagement 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 (EHR...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-02-01
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| Series: | JMIR Medical Informatics |
| Online Access: | https://medinform.jmir.org/2023/1/e40964 |
| _version_ | 1797734283398348800 |
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| author | Simon Gaviria-Valencia Sean P Murphy Vinod C Kaggal Robert D McBane II Thom W Rooke Rajeev Chaudhry Mateo Alzate-Aguirre Adelaide M Arruda-Olson |
| author_facet | Simon Gaviria-Valencia Sean P Murphy Vinod C Kaggal Robert D McBane II Thom W Rooke Rajeev Chaudhry Mateo Alzate-Aguirre Adelaide M Arruda-Olson |
| author_sort | Simon Gaviria-Valencia |
| collection | DOAJ |
| description |
BackgroundManagement 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.
ObjectiveThis 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.
MethodsThe 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.
ResultsA 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).
ConclusionsImplementation 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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| first_indexed | 2024-03-12T12:41:00Z |
| format | Article |
| id | doaj.art-0b07e2770cde4945b22e21b0472cc5d8 |
| institution | Directory Open Access Journal |
| issn | 2291-9694 |
| language | English |
| last_indexed | 2024-03-12T12:41:00Z |
| publishDate | 2023-02-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Medical Informatics |
| spelling | doaj.art-0b07e2770cde4945b22e21b0472cc5d82023-08-28T23:47:06ZengJMIR PublicationsJMIR Medical Informatics2291-96942023-02-0111e4096410.2196/40964Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation StudySimon Gaviria-Valenciahttps://orcid.org/0000-0003-3156-2412Sean P Murphyhttps://orcid.org/0000-0002-6582-4065Vinod C Kaggalhttps://orcid.org/0000-0003-0944-2211Robert D McBane IIhttps://orcid.org/0000-0001-8727-8029Thom W Rookehttps://orcid.org/0000-0003-3089-3156Rajeev Chaudhryhttps://orcid.org/0000-0003-1249-5656Mateo Alzate-Aguirrehttps://orcid.org/0000-0003-1129-238XAdelaide M Arruda-Olsonhttps://orcid.org/0000-0001-9541-9899 BackgroundManagement 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. ObjectiveThis 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. MethodsThe 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. ResultsA 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). ConclusionsImplementation 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. https://medinform.jmir.org/2023/1/e40964 |
| spellingShingle | Simon Gaviria-Valencia Sean P Murphy Vinod C Kaggal Robert D McBane II Thom W Rooke Rajeev Chaudhry Mateo Alzate-Aguirre Adelaide M Arruda-Olson Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study JMIR Medical Informatics |
| title | Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study |
| title_full | Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study |
| title_fullStr | Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study |
| title_full_unstemmed | Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study |
| title_short | Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study |
| title_sort | near real time natural language processing for the extraction of abdominal aortic aneurysm diagnoses from radiology reports algorithm development and validation study |
| url | https://medinform.jmir.org/2023/1/e40964 |
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