This project seeks to develop a natural language understanding system specifically aimed at extracting relevant clinical facts from medical reports. The system is based largely on a semantic parsing technique that stresses the use of medical knowledge encoded in four forms. These forms include: a hierarchy of terms embedded in a general purpose medical data dictionary; a semantic network designed to capture knowledge concerning the relative locations of different anatomic sites; a collection of frames specifying allowable combinations of terms. These frames also have a hierarchial organization designed to help the parser find an appropriate format for the recognition and storage of a complex medical fact; a transformational grammar attached to the hierarchy of frames which can propose the different ways a medical fact, as indicated by the combined terms in a frame, might be expressed; a causal network developed specifically to allow disambiguation of the many incompletely expressed facts that can be found in a medical report. Both a lexicon expressing the different words known to the system and a thesaurus expressing all meaningful phrases expected in the reporting domain will also be built. A system that uses this information to parse medical text will be constructed and evaluated. The domains tested will be the reports of chest x-rays and admitting history and physical examination for patients with pulmonary and/or cardiac diseases. The evaluation will determine whether relevant medical facts presented in the reports are captured and stored by the natural language parser in an integrated, general purpose medical data base. The goal of this project is to further techniques that allow the encoding of medical information captured as free text into a form appropriate for research, quality, assurance, and direct clinical decision support.
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