title: Automatically correlating clinical findings and body locations in radiology reports using MedLEE.
abstract: In this paper, we describe and evaluate a system that extracts clinical findings and body locations from radiology reports and correlates them. The system uses Medical Language Extraction and Encoding System (MedLEE) to map the reports' free text to structured semantic representations of their content. A lightweight reasoning engine extracts the clinical findings and body locations from MedLEE's semantic representation and correlates them. Our study is illustrative for research in which existing natural language processing software is embedded in a larger system. We manually created a standard reference based on a corpus of neuro and breast radiology reports. The standard reference was used to evaluate the precision and recall of the proposed system and its modules. Our results indicate that the precision of our system is considerably better than its recall (82.32-91.37% vs. 35.67-45.91%). We conducted an error analysis and discuss here the practical usability of the system given its recall and precision performance.
authors.affiliation: Philips Research Europe, Prof. Holstlaan 4, 5656AA, Eindhoven, the Netherlands. merlijn.sevenster@philips.com
authors.forename: Merlijn
authors.initials: M
authors.lastname: Sevenster
authors.forename: Rob
authors.initials: R
authors.lastname: van Ommering
authors.forename: Yuechen
authors.initials: Y
authors.lastname: Qian
mesh_terms: D001927:Brain Diseases / Q000000981:diagnostic imaging; D001941:Breast Diseases / Q000000981:diagnostic imaging; D057225:Data Mining; D006801:Humans; D009323:Natural Language Processing; D011859:Radiography; D011871:Radiology; D011873:Radiology Information Systems; D012984:Software