Knowledge Discovery and Data Mining of Free Text Radiology Reports

Medical Knowledge Discovery and Data Mining (KDD) over text is a promising yet difficult technology for unlocking meaning and uncovering associations in vast clinical text repositories. We report our experience in developing a new text analytic system called MEDAT or Medical Exploratory Data Analysis over Text, which overcomes several problems in text mining. The MEDAT system employs an annotated semantic index with a large number of assertions (propositions). The semantic index is able to capture complex assertions which encapsulate conceptual relationships including their modifiers at a granular level. The index represents semantically equivalent sentences with the same symbols, a necessary component for KDD semantic queries, including semantic Boolean and correlation queries. The graphical user interface enables users to perform complex semantic analysis of the Roentgen corpus, consisting of 594,000 de-identified radiology reports with 4.3 million sentences, without having to learn a programming language. The MEDAT architecture offers a novel framework for text mining in other medical domains.