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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  
Speaker:
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
URL:
Location: 5317 Sennott Square
Groups Posted: Computational/Systems Biology, Bioinformatics Intelligent Systems Program Machine Learning Group 
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

Interest Area: Biological Sciences, Computer & Information Science & Engineering, General Interests, Health Sciences
 
 
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