Automated diagnostic indexing by natural language processing

Developing tools for natural language understanding by computers represents an important and intense field of research. This paper describes a system developed for interpreting medical natural language in the domain of symptoms and diagnoses from complete discharge summaries and locating the correspondent category into the International Classification of Diseases, through indexing by the Systematized Nomenclature of Medicine. The indexing program makes use of the MEID dictionary and some auxiliary semantic databases for identifying adjectival forms, synonyms, hypernyms and other semantic relations while searching for the longest consistent match into SNOMED. A further subdivision of the SNOMED structure was also proposed in order to find the hierarchically superior representative of a conceptual class when this association is not assigned by the related SNOMED code number. The system can be used by any language that possesses a translation of SNOMED and ICD. The knowledge base was built using a conversion file that maps the terms of the nomenclature into the classification, which can be improved by learning from users.