NLP Detection of Critical Radiology Recommendations

Faculty Lead(s)

• Meliha Yetisgen

Project Summary

Communication of recommendations and abnormal test results is prone to error. If important imaging findings and recommendations are not systematically identified and promptly communicated to referrers, poor patient outcomes can result. In this project, we investigate NLP and supervised classification approaches to identify critical recommendation sentences in radiology reports.

Project Keywords: Clinical Informatics