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Posted: ISP Admin on  Nov 08 03:00:43 PM
Title: Identifying Incidental Findings from Radiology Reports of Trauma Patients: An Evaluation of Automated Feature Representation Methods  
Gaurav Trivedi
Intelligent Systems Program
Sponsor: University of Pittsburgh  >  School of Computing and Information  >  Intelligent Systems Program
Series: ISP Colloquium Series
Date: Nov 30, 2018 12:30 PM - 1:00 PM
Location: 5317 Sennott Square
Groups Posted: Computational/Systems Biology, Bioinformatics Intelligent Systems Program Machine Learning Group 

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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