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Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset.
- Source :
-
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science [AMIA Jt Summits Transl Sci Proc] 2020 May 30; Vol. 2020, pp. 335-344. Date of Electronic Publication: 2020 May 30 (Print Publication: 2020). - Publication Year :
- 2020
-
Abstract
- Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. In this paper, we present a natural language processing approach based on deep learning to automatically identify clinically important recommendations in radiology reports. Our approach first identifies the recommendation sentences and then extracts reason, test, and time frame of the identified recommendations. To train our extraction models, we created a corpus of 1367 radiology reports annotated for recommendation information. Our extraction models achieved 0.93 f-score for recommendation sentence, 0.65 f-score for reason, 0.73 f-score for test, and 0.84 f-score for time frame. We applied the extraction models to a set of over 3.3 million radiology reports and analyzed the adherence of follow-up recommendations.<br /> (©2020 AMIA - All rights reserved.)
Details
- Language :
- English
- ISSN :
- 2153-4063
- Volume :
- 2020
- Database :
- MEDLINE
- Journal :
- AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
- Publication Type :
- Academic Journal
- Accession number :
- 32477653