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Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset
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...
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| Gepubliceerd in: | AMIA Jt Summits Transl Sci Proc |
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| Hoofdauteurs: | , , , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
American Medical Informatics Association
2020
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7233090/ https://ncbi.nlm.nih.gov/pubmed/32477653 |
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