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Professor and Vice Chair of Radiology
University of Pennsylvania
Abstract: We evaluated the ability to detect causal associations among diseases and imaging findings from radiology reports. We analyzed 1,702,462 consecutive reports of 1,396,293 patients for positive mention of 16,839 entities (disorders and imaging findings) of the Radiology Gamuts Ontology (RGO). The approach, which combined NLP techniques to identify relevant terms and Bayesian networks to infer causality identified 725 pairs of entities as causally related; 634 were confirmed by reference to RGO or physician review (87% precision). This approach finds causal relationships with high precision from textual radiology reports, despite the fact that causally related entities represented only 0.039% of all pairs of entities. Applying this approach to larger report text corpora may help detect unspecified or hertofore unrecognized associations.
About: Charles E. Kahn Jr., MD, MS is Professor and Vice Chair of Radiology at the University of Pennsylvania Perelman School of Medicine. He served on the faculty of the University of Chicago and the Medical College of Wisconsin before moving to his current position at the University of Pennsylvania. He is a board-certified practicing radiologist with expertise in body CT and ultrasound. Professional interests include health services research, comparative effectiveness research, artificial intelligence, decision support, information standards, and knowledge representation. He has received the Gold Medal of the American Roentgen Ray Society and has been elected a Fellow of the American College of Radiology, American College of Medical Informatics, and Society for Imaging Informatics in Medicine. He authored more than 120 scientific publications and serves as Editor of Radiology: Artificial Intelligence. He earned his Bachelor of Arts in Mathematics at the University of Wisconsin‚ Madison, his MD at the University of Illinois, and completed radiology residency at the University of Chicago, where he served as chief resident. He earned a Master’s degree in Computer Sciences from UW-Madison in 2003.
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