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Posted: |
ISP Admin on
Nov 08 03:00:43 PM
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Title: |
Identifying Incidental Findings from Radiology Reports of Trauma Patients: An Evaluation of Automated Feature Representation Methods
Bookmarked
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Speaker: |
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Sponsor: |
University of Pittsburgh
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School of Computing and Information
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Intelligent Systems Program
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Series: |
ISP Colloquium Series
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Date: |
Nov 30, 2018 12:30 PM - 1:00 PM
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URL: |
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Location: |
5317 Sennott Square |
Keywords: |
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Groups Posted: |
Computational/Systems Biology, Bioinformatics Intelligent Systems Program Machine Learning Group |
Bookmarked by: |
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Detail: |
Radiologic imaging of trauma patients often uncovers findings that are unrelated to the trauma. Identifying these incidental findings in clinical notes is necessary for proper follow-up. We developed and evaluated an automated pipeline to identify incidental findings in radiology reports of trauma patients at the sentence and section levels using a variety of feature representations. We annotated a corpus of over 4,000 reports and investigated several feature representations including traditional word and concept (such as SNOMED-CT) representations as well as word and concept embeddings. We evaluated these representations using traditional machine learning as well as CNN-based deep learning methods. Our results show that the best performance was achieved by using CNNs with Pre-trained embedding at both sentence and section levels. This provides evidence that such a pipeline is likely to be clinically useful to identify incidental findings in radiology reports in trauma patients
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