Natural Language Generation from SNOMED Specifications.

SNOMED (Systematized Nomenclature of Medicine) is a compre- hensive clinical terminology that contains almost 400,000 concepts, since SNOMED is a formal language; it is hard to understand for users who are not acquainted with the formal specifications. Natural language generation (NLG) is a technique utilizing computers to create natural language descriptions from formal languages. In order to generate descriptions of SNOMED concepts, two NLG tools were implemented for the English and Swedish version of SNOMED respectively. The one for English used a natural language generator called ASTROGEN to produce description texts. This tool also applied several aggregation rules to make the texts shorter and easier to understand. The other tool used C#.Net as the programming language and applied a template-base generation technique to create concepts explanation in Swedish. As a base line same SNOMED concepts were presented in a tree structure browser. To evaluate the English NLG system, 19 SNOMED concepts were randomly chosen for the generation of text. Ten volunteers participated in this evaluation. Five of them estimated the accuracy of the texts and others assessed the fluency aspect. The sample texts got a mean score 4.37 for accuracy and 4.47 for fluen- cy (max 5 score). To evaluate the Swedish NLG system, five concepts were randomly chosen for the generation of texts. In parallel two physicians with knowledge in SNOMED created manually natural language descriptions of the same concepts. Both manual and system generated natural language descriptions were evaluat- ed and compared by in total four physicians. All respondents scored the manual natural language descriptions the highest in average 83 of 100 scores while the system generated natural language texts obtained around 68 of 100 scores. All three respondents unanimously except one respondent (scoring 7 of 10) pre- ferred the system-generated text. This paper presents a possible way using Natural Language Generation to explain the meaning of SNOMED concepts for people who are not familiar with SNOMED formal language. The evaluation results indicate that the NLG techniques can be used to implement this task.